Cendrine Mony
email: cendrine.mony@univ-rennes1.fr

Quentin Landais

Romain Georges

Alain Butet

Françoise Burel

Olivier Jambon

Valérie Gouesbet

& Aude Ernoult

Effects of connectivity on seed dispersal patterns in hedgerows Authors

Keywords: Ecological corridors, landscape connectivity, seed dispersal, seed predation, least-cost path analysis, plant community, hedgerow network, forest fragments

Habitat isolation is a major driver of biodiversity as it affects dispersal among local communities, especially for plants. Corridors are supposed to facilitate the movement of organisms and hence their effective dispersal, thereby increasing biodiversity. Hedgerows provide favorable habitats for forest plant establishment, but their role in dispersal among forest patches is still unknown. In addition, seeds can be more predated within hedgerows as animal movement is also encouraged by these corridors. We are testing the following question: Are plant dispersal and seed removal by animals in connected hedgerows compared to unconnected hedgerows promoted for forest plant species?

Location

Long Term Ecological Site of "Zone Atelier Armorique", Western France (ca. 150 km 2 )

Methods

We analyzed seed dispersal along two pathways in 16 woodlots, connected (least cost pathways, i.e., optimal corridors) and unconnected high cost pathways, (absence of corridor). Using a trap study, we first quantified seed rain and analyzed seed composition after trapped seeds were germinated in the greenhouse. At the same sampling points, we then analyzed the rate at which two different sized seeds were removed from seed baits by animals.

Results

Our results revealed no difference in the number of trapped seeds in connected and unconnected pathways, but did reveal a difference in the number and richness of viable seeds, corridors having a positive impact. In the predation experiment, the rate of removal of small seeds was higher in connected pathways than in unconnected pathways.

Conclusion

Connectivity enhanced the dispersal of viable forest plant species but also the predation of small seeds, which possibly played a role in their subsequent dispersal. This work highlighted the effect of connectivity on seed dynamics within hedgerows and improves our understanding on their role as stepping stones for seed dispersal among forest patches.

Introduction

Habitat isolation is an important driver of species assemblages as it limits dispersal among local patches and affects the structure of metacommunities [START_REF] Leibold | The metacommunity concept: a framework for multi-scale community ecology[END_REF]). In small wooded habitats, plant assemblages are particularly sensitive to habitat isolation (Grasholf-Bokdam, 1997;[START_REF] Cordeiro | Low recruitment of trees dispersed by animals in African forest fragments[END_REF] as they are strongly driven by dispersal limitation [START_REF] Honnay | Permeability of ancient forest edges for weedy plant species invasion[END_REF][START_REF] Verheyen | Response of forest plant species to land-use change: a life-history trait-based approach[END_REF]. Connectivity (sensu [START_REF] Taylor | Connectivity is a vital element of landscape structure[END_REF] provided by the landscape mosaic is assumed to promote organisms' dispersal and to offset the negative consequences of habitat isolation by enhancing gene flow and favoring the likelihood of (re)colonization of habitat patches [START_REF] Young | The population genetic consequences of habitat fragmentation for plants[END_REF]. However, the effect of connectivity on species assemblage has been less frequently studied in plants than in animals [START_REF] Gilbert-Norton | A Meta-Analytic Review of Corridor Effectiveness[END_REF][START_REF] Fletcher | Divergent perspectives on landscape connectivity reveal consistent effects from Genes to communities[END_REF], with few emphasis on community-wide approaches (Uroy et al., 2019a), especially for forest plant species (but see [START_REF] Mony | Effects of connectivity on animal-dispersed forest plant communities in agriculture-dominated landscapes[END_REF].

Linear woody elements, such as hedgerows have been suggested to provide favorable habitat conditions for forest species [START_REF] Mccollin | Hedegerows as habitat for woodland plants[END_REF]Wehling and Diekmann, 2009).

Through a large European study, [START_REF] Litza | Hedgerows as a habitat for forest plant species in the agricultural landscape of Europe[END_REF] demonstrated the important role of hedgerows as refuge for many forest plant species. In addition, hedgerows provide more stable features in agricultural landscapes than the other features constituting the landscape mosaic and may promote species establishment and subsequent dispersal. They may also influence the vectors (e.g. animal, wind) involved in plant species dispersal [START_REF] Uroy | Additive effects of the connectivity provided by different habitat types drive plant assembly[END_REF]. Because plant species may disperse through a stepping-stone process, hedgerows should then enhance connectivity among forest patches and constitute preferential pathways for species to be dispersed, especially when they are connected to source patches (Lenoir et al., 2021).

Though, species effective dispersal within hedgerows is not well-known. Recent works demonstrated that species richness in plant assemblages harbored in hedgerows, especially of forest species depends on their age [START_REF] Paal | Both spatiotemporal connectivity and habitat quality limit the immigration of forest plants into wooded corridors[END_REF][START_REF] Lenoir | Historical continuity and spatial connectivity ensure hedgerows are effective corridors for forest plants: Evidence from the species-time-area relationship[END_REF] as ancient hedgerows harbor also species that have low dispersal capacity (Closset-Koop et al., 2016). Species composition and richness depends on their distance to the forest patch [START_REF] Corbit | Hedegerows as habitat corridors for forest herbs in central New York, USA[END_REF]Wehling and Diekmann, 2009). One limitation of these correlative approaches is that plant communities established within hedgerows depend on both dispersal (i.e., plant recruitment via seeds) and local factors (i.e., the ability of seeds to germinate and establish in the hedgerow patch).

Then, species assemblages occurring in hedgerow might not reflect the actual dispersal within the hedgerow. In addition, the efficiency of seed dispersal could also depend on predator and or

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parasitism pressure, that could lower the flow of viable seeds (i.e., able to germinate), or increase the predation rate of seeds in situ.

The aim of the present study was to analyze the role of hedgerows in the actual dispersal of plants at different distances from 16 source forest patches in two contrasted connectivity configurations: highly connected and unconnected hedgerow pathways. These two connectivity configurations were predicted using resistance-based models (least-cost path analysis) [START_REF] Adriaensen | The application of 'least-cost' modelling as a functional landscape model[END_REF]. We conducted two complementary experiments to evaluate actual seed dispersal by analyzing seed flows and their in-situ predation. In the first study, we analyzed the composition and richness of dispersed seeds using seed traps. This trapping study evaluated seed rains originating from plants that dispersed along the hedgerow corridor, but also seed viability through an emergence experiment in the greenhouse. The second study quantified the number of seeds eaten, which is related to predation by animals. We measured a proxy of seed-predation pressure for two model species, millet (Panicum miliaceum) and buckwheat (Fagopyrum esculentum).

These species were tested because they are absent in the study landscape and their seeds are similar in size to the seeds of forest plant species that are dispersed naturally. We performed both studies in fall and winter to cover peak seed dispersal periods. More specifically, we tested the following hypotheses:

(1) Seed dispersal (i.e., number of seeds trapped) is higher in connected pathways than in the unconnected pathways, with a lower positive effect of corridors on viable seed fluxes due to enhanced parasitism and predation in corridors. Species composition and richness depend on the type of pathway present, with more species and more seeds occurring in connected pathways than in the unconnected pathways (Trapping study).

(2) More seeds are removed by animal predation along connected pathways than along unconnected pathways (Seed removal study).

Methods

Sampling design

We performed this study in the "ZA Armorique" long-term socio-ecological research (LTSER) site in Western France (48°36′N, 1°32′W). This area covers 15,000 ha and is an agriculturedominated landscape with less than 15% of forest cover and a well-developed network of hedgerows. We selected 16 small forest patches (woodlots) based on: (1) their size (fragments ranged from 1-5 ha in size), and (2) their shape; to limit edge effects, all the plots had a low

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perimeter/surface ratio < 0.04; both criteria were around the median for the woodlots in the area.

Woodlots were post-agricultural (i.e., they were established on former agricultural land) and were dominated by Fagus sylvatica, Quercus robur and Castanea sativa. The woodlots are extensively managed and embedded in a hedgerow network mainly composed of old hedgerows already present in historical photographs of 1952 (the oldest images available for this area).

From each woodlot, we defined two different pathway types, one corresponding to hedgerow network with few or no interconnections (hereafter called an "unconnected" pathway) and the other to a better connected network (hereafter called a "connected" pathway, and corresponding to the least cost path linking woodlots which are the source habitat patches, and found using resistance-based connectivity models) (Fig. 1). Both pathways were identified from land-cover maps, originating from photo interpreted aerial imagery (French National Institute of Geographic and Forest Information) combined with object-based, remote sensing imagery acquired by RapidEye satellites. We identified eight land-use categories, each at 5-m resolution: cropland, semi-natural grassland, managed grassland, woodlot and forest, urban cover, water body, hedgerow, and road. The hedgerow network and road categories were identified from the BDTopo® vector geographic database (2003)(2004)(2005)(2006), produced by the French National Institute of Geographic and Forest Information. The permeability values attributed to each land-use category were based on Watts et al. (2010) and used successfully by [START_REF] Gil-Tena | Woodland bird response to landscape connectivity in an agriculture-dominated landscape: a functional community approach[END_REF] and [START_REF] Mony | Effects of connectivity on animal-dispersed forest plant communities in agriculture-dominated landscapes[END_REF] for this study area. Permeability values were fixed based on the vertical structure of the habitat, which determines its permeability to the movement of forest woodland species. Least cost paths (LCPs) were computed from the permeability map using a minimum planar graph (Graphab, [START_REF] Foltête | A software tool dedicated to the modelling of landscape networks[END_REF] and calculated among all the sampled woodlots. For each woodlot, we selected the connected and unconnected pathways based on these calculations: the connected pathway was defined as LCP, whereas the unconnected pathway was selected in the area where no LCP was predicted (i.e., area with low permeability values), and at a maximum distance from the connected pathway to guarantee spatial independence (Fig. 1). Unconnected pathways comprised hedgerow fragments that were spatially discontinuous and embedded in land-use areas with highresistance values (croplands, urban cover). In contrast, connected pathways were mostly continuous hedgerows embedded in land-use areas with low-resistance values (woodlands and grasslands). To quantify the distance-dependent colonization of seeds into hedgerows that are either connected or unconnected to a woodlot patch, we analyzed seed flows along each pathway. To this end, we placed seed traps at 75m intervals at four sampling points (at 300-m intervals) along each pathway (32 pathways x 4 points per pathway, i.e. 128 sampling points), starting 75m from the edge of each woodlot. This sampling design was intended to assess the spatial dispersal kernel of plant seeds, under the assumption that dispersed seeds are most abundant closest to the source [START_REF] Nathan | Long-distance dispersal of plants[END_REF]. We assumed that trapped seeds were produced by plants migrating from the forest patches along the ecological pathways. All four sampling points were located in a hedgerow habitat to standardize local habitat conditions. On some pathways, hedgerow fragments were not available at the defined distances. In 20 of the 128 sampling points, seed plots were then located in the mostnatural interface within the pathway at the given distance (e.g., field margin). At 13 of the 128

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sampling points, no possible natural interfaces were available and those points were consequently not sampled.

Measuring seed flows in connected and unconnected pathways (Trapping study)

Seed traps (4 sampling points per path) were made from 25 x 25 cm polyethylene artificial turf mats (625 cm2), [START_REF] Wolters | Plant colonization after managed realignment: the relative importance of diaspore dispersal[END_REF][START_REF] Sarneel | The role of wind in the dispersal of floating seeds in slow-flowing or stagnant water bodies[END_REF]. At each of the four seed-trap points, 15 replicate turf mats were placed on the ground in the hedgerow (inside and at the edges) from mid-November 2013 to mid-January 2014 (10-week-period). This sampling period coincided with peak seed dispersal following summer seed release. In order to minimize potential local effects of seed deposition, we removed all herbaceous vegetation within 2 m of each trap. In addition, we classified the hedgerow at the plot scale using a six-class index (1) no hedgerow, (2) relict hedgerow with a dense cover of Rubus spp. and Pteridium spp., (3) hedgerow with sparse trees and a short herbaceous layer, (4) hedgerow with sparse trees and a tall herbaceous layer, ( 5) hedgerow with a dense tree or shrub layer, and ( 6) hedgerow with both dense tree and shrub layers. At the end of the sampling period, the plastic mats were collected in separate bags and taken to the laboratory for analysis. Each trap was vacuumed to collect the material trapped in the mats. The material collected from the mats was sieved successively at three different mesh sizes (2, 1 and 0.5 cm) to collect the seeds. With this sieving step, we discarded from following analyses seeds coming from Fagus sylvatica, Quercus robur and Castanea sativa that were retained by the first mesh size. These species were planted for constituting the hedgerows. Then, we considered that they were not representative of spontaneous colonization of plants in these landscapes. We counted all the seeds, even those that were obviously not capable of germinating (e.g., those parasitized or partially eaten by predators). These seeds were termed "trapped seeds."

Characterizing dispersed seed assemblages in connected and unconnected paths (Trapping study)

All the seeds collected in each plot (n = 115) were spread out in a thin layer (< 5 mm thick) on trays filled with sterile potting soil topped with a 4-mm layer of sterile sand, following the method described by ter Heerdt et al. (1996). The samples were moved to a greenhouse maintained at a daytime temperature of 25 °C and nighttime temperature of 15 °C and watered every day. For each batch of 10 trays, a control tray containing no seeds was included to quantify seeds that could have blown onto the trap after it was placed in the greenhouse. Emerging seedlings were identified, counted, and removed as soon as possible. Seedlings that could not be identified were transferred

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to separate pots and allowed to grow until they could be identified. When all germination had ceased, the soil was crumbled by hand and kept for two more weeks to allow any seeds that remained in the soil to germinate. We applied this crumbling procedure to expose seeds buried more deeply in the soil and to reduce the risk of seeds remaining dormant. The germination study ended in December 2014. We termed seeds that had been trapped, counted, and identified after germinating in the greenhouse "emerged seeds". At each sampling point (representing 15 seed traps), we measured species richness and abundance of seedlings at three levels of resolution (1) all plant species (i.e., all forest and non-forest species), (2) forest species [species listed in the French Forest flora of [START_REF] Rameau | Flore forestière française[END_REF]], and (3) animal-dispersed forest species [zoochorous species listed in the Baseflor database [START_REF] Julve | Baseflor. Index botanique, écologique et chorologique de la flore de France[END_REF]]. The species composition of the trapped seeds was compared with the flora of the source woodlots using data published in [START_REF] Mony | Effects of connectivity on animal-dispersed forest plant communities in agriculture-dominated landscapes[END_REF] to check whether both species pools were similar. However, for a given sampling point, we did not specifically relate forest species trapped to those occurring in the corresponding woodlot patch to which the sample point was related. We used the French taxonomic online database TAXREF v12.0 as scientific nomenclature of plant taxa.

Analyzing seeds predated by animals in connected and unconnected pathways (Seed removal study)

As model species, we used two types of seeds belonging to two different taxonomic families, millet (Panicum miliaceum; Poaceae) and buckwheat (Fagopyrum esculentum ; Polygonaceae).

We selected these species because they were included within the size range of the seeds we had trapped in the trapping study (Table S1). Seeds for P. miliaceum weigh 4-6.5 mg and measure approximatively 3 x 2 mm. Seeds for F. esculentum weigh 25-30 mg and measure approximatively 6 x 3.7 mm. We selected the model species based on their seed size only, as this trait was shown in a recent meta-analysis to be a good proxy of seed removal by predators [START_REF] Dylewski | Seed size predicts global effects of small mammal seed predation on plant recruitment[END_REF].

We also chose these species because they were not present in the surrounding landscape and could not colonize spontaneously traps through natural seed rain. We performed a seed predation experiment in 15 for buckwheat and 14 for millet out of the 16 woodlots used in the trapping study. We counted the remaining seeds in the seed baits at the same sampling points. We analyzed seed removal during two campaigns, the first in February 2014 and the second in October 2014, which corresponded with the sampling campaign of the prior trapping study. At each sampling point, we placed two sets of 20 millet seeds on two artificial turf seed traps (total 40 seeds) and

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two sets of 20 buckwheat seeds on two other traps. At Days 2 and 6 after depositing the seeds, we measured the removal rate, defined as the proportion of the original 40 seeds that were missing.

We assumed that the removal rate was an indicator of animal predation, despite the selection of plant species that do not correspond to forest species, and that have gone through the domestication filter.

Statistical analysis

Prior to analyses, we analyzed the independence of the ecological variables through pairwise correlation analysis (Pearson correlations). The species richness of all species at each point was closely correlated with the species richness of forest species (R = 0.91). The abundances of trapped species were closely correlated with the seed abundance of emerged species (R = 0.96) and of the emerged forest species (R = 0.99). We also analyzed the dependence of hedgerow structure at local sampling points based on the type of pathway, with chi-squared tests based on the number of sampling points per index category. Hedgerow structure at sampling points differed significantly between connected and unconnected pathways (Chi-squared = 24.68; p < 0.001) (Fig. 2).

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To test for the effect of connectivity on dispersed plant assemblage indices (number of seeds, plant species richness, and abundance) (Trapping study), and on seed removal rate (Seed removal study), we used mixed models. Models tested for the effect of the type of pathway (connected vs. unconnected, qualitative variable), position along the pathway (from one to four, qualitative variable), and their interaction as fixed effects, and pathway type nested within the woodlot source patch as random effects (Explained variable~pathway type * position + 1|source patch/pathway type). For most explained variable tested, data showed a strong overdispersion.

Especially in the seed removal study, seeds were absent at many of the sampling points (especially buckwheat). We therefore used generalized linear mixed-effects models with a log link function and a negative binomial distribution, which are adapted to count data and yielded GLMs without overdispersion [START_REF] Zuur | Mixed effects models and extensions in ecology with R[END_REF]. In the seed removal study, lack of convergence was detected for one model for buckwheat (seed remaining at Day 6 in February). For all models used in both studies, we tested the model including all variables. We analyzed the significance of each variable within the model through ANOVA type II sums of squares. We calculated the proportion of variance explained by the fixed effect (marginal R²) and by random effects (conditional R²) (sensu [START_REF] Nakagawa | A general and simple method for obtaining R² from generalized linear mixed-effects models[END_REF]. We graphically checked for homoscedasticity, independence and normality of residuals for the tested models. We performed all statistical analyses using the following software packages in R: lme4, nmle, car, glmmTMB, piecewiseSEM.

Results

Trapping study: Seed flows in connected and unconnected pathways

Dispersed species assemblages were characterized by a large number of trapped seeds (Fig. 3), with high variability among sampled points, ranging from zero to 14,475 seeds/m². After the greenhouse emergence study, the germination rate of the trapped seeds was low, ranging from 0 to 57% (mean germination rate: 9.2% ± 10.6%). We recorded 91 plant species in the emergence study with 42 species that are also present in the flora of the woodlots. Seed fluxes were dominated mostly by Populus tremula, Galium aparine, Alnus glutinosus (5-37% of the total number of emerged seeds), and to a lesser extent by Dactylis glomerata, Chenopodium album, Rumex crispus, Holcus lanatus, Poa pratensis, Urtica dioica, Epilobium tetragonum and Persicaria lathifolium (1-5% of the total number of emerged seeds, Appendix S1). Within these 91 plant species, forest species represented 78.6% ± 27.4% of species richness and 83.9% ± 26.4 of Accepted Article seedling abundance. Within forest species, animal-dispersed species represented 60.9% ± 36.6% of species number and 62.9% ± 39.6 of seedling abundance. The total abundance of trapped seeds was independent of the type of pathway and distance from the woodlot. However, abundance varied widely, especially in the connected pathway. One sampling point (Corridor pathway type) displayed especially a very high density in seeds (14, 475 seed/m²) due to a high number of P. tremula seeds. P. tremula belonged to the category of nonanimal dispersed forest species. Because statistical models did not converge for the seed number

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of emerged seeds of all and forest species owing to this atypical value, we tested the relationships discarding this point (Fig. 3 did not include this atypical value). Relationships were kept identical (Table 1).

Species richness and the number of emerged seeds of all species and of forest species were significantly higher in the connected than in the unconnected pathway (statistical tendency for seed abundance of forest species), whereas no effect was found for animal-dispersed forest species (Fig. 3; Table 1). The distance to the woodlot and its interactive effect with pathway type were not related to any of the measured variables (Table 1).

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Table 1 Effect of type of pathway and distance from woodlot source (16 woodlots) on seed flows and species richness of dispersed seed assemblages.

"Trapped seeds" are those intercepted by seed traps; "emerged seeds" are seeds that were trapped and subsequently germinated in the greenhouse.

The number of seeds is expressed per m². . Given model parameters are the Chi-square statistic (Chisq) and p-values from ANOVA II, the marginal and complete R² (R²m; R²c) and the AIC. Variables in bold and in bold-italics are statistically significant (p < 0.05) and tend to be significant (p<0.

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Seed removal study: Seed removal by animals in connected and unconnected pathways Fewer buckwheat seeds (mean = 7.0 +/-12.3 seeds/point) than millet seeds (mean = 16.5 +/-11.3 seeds/point) remained after six days in both survey periods. More millet seeds remained in the unconnected pathway than in the connected pathway, independent of the distance from woodlot (Fig. 4; Table 2). Seed predation was higher in the connected pathways than in the unconnected pathways. This effect occurred on Day 2 and on Day 6 in the October samples and only on Day 6 in the February samples. We found no effect of the type of pathway or distance from the woodlot for buckwheat seeds except for Day 2 in the October samples (Table 2, Fig. 3).

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Discussion

Effect of corridors on seed dispersal patterns

Contrary to our expectations, we found no difference in the quantity of dispersed seeds between connected and unconnected pathways, but the number of germinating seeds (i.e. viable seeds) did differ, as the connected pathways contained more seeds than the unconnected pathways. The quantity of trapped seeds was surprisingly high (mean ~ 300 seeds/m²) and highly variable, especially in the connected pathways. This variability was due in some sampling points to seeds produced by a few tree species (e.g., Populus tremula). P. tremula is common in the woodlots of our study area and produces many small seeds that are efficiently dispersed by the wind. For this species and other wind-dispersed species, hedgerows likely affect wind dynamics by redirecting airflow and increasing the likelihood of wind-blown seed being intercepted by hedgerows [START_REF] Damschen | How fragmentation and corridors affect wind dynamics and seed dispersal in open habitats[END_REF]. Such effects may depend on local hedgerow characteristics and connectivity patterns leading to the high variability we observed in the number of trapped seeds.

Despite the high seed flows we recorded, most seeds did not germinate (the mean germination rate was only 10%), i.e. lower than the mean germination rate recorded for these plant species in optimal conditions (TRY database; [START_REF] Kattge | TRY -a global database of plant traits[END_REF]. This difference in germination success suggests that parasitism or partial predation has a strong impact on dispersed seeds and may impede effective dispersal along corridors.

Forest species (e.g. species listed in the French forest flora of [START_REF] Rameau | Flore forestière française[END_REF] represented the majority of the germinating seeds in our study. Our results showed that the type of pathway had a positive influence on seed abundance and species richness for whole assemblages and particularly for forest species assemblages (statistical tendency for seed abundance). This effect of connectivity supports our hypotheses that continuous hedgerow corridors promote the dispersal of forest species by enhancing their migration. Connectivity is however not totally independent from the hedgerow local habitat conditions as we demonstrated that hedgerows sampled in the connected pathways had a more complex structure (with multiple layers of trees, shrubs, and herbaceous species) than hedgerows in the unconnected pathways. This might affect the effective dispersal of species that use corridors as alternative habitats along their dispersal path among forest patches. Surprisingly however, animal-dispersed plants were not favoured by corridors but rather tended to depend on the distance to source-patch. This absence of connectivity effect on animal-dispersed forest plant species may be related an overall low density of animalvectors in this landscape, especially those dispersing forest plants, which would have to be

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investigated in a further work. This could be also related to predation pressure during the sampling period that might differ between connected and unconnected hedgerows (see results from seed removal study).

Effect of corridors on seed predation

Our results showed that the removal rate especially of small seeds was higher in connected pathways than in unconnected pathways. These results suggest that connectivity favors the movement of granivorous animals, and primarily those that feed on small seeds (i.e., small animals such as carabids, rodents, or birds). In this landscape, woodlots are small and have limited carrying capacity [START_REF] Gil-Tena | Woodland bird response to landscape connectivity in an agriculture-dominated landscape: a functional community approach[END_REF]. Therefore, the animals that frequent them tend to be small and have restricted ranges. These species might predate seeds and partly offset the higher number of seeds dispersed along corridors; but they can also disperse some of the seeds. Such dispersal has been demonstrated at short distances: for instance, rodents disperse small seeds less than 30 m [START_REF] Cain | Seed dispersal and the holocene migration of woodland herbs[END_REF][START_REF] Xiao | Impacts of scatter-hoarding rodents on restoration of oil tea Camellia oleifera in a fragmented forest[END_REF], by means of seeds attached to their fur (Kiviniemi and Telenius, 1998) or for scatter hoarding seeds [START_REF] Wang | Scatter-hoarding rodents select different caching habitats for seeds with different traits[END_REF]). Bird disperse seeds while feeding and by hiding fruits and nuts for winter. Dispersal distances are difficult to assess in both cases but are likely to range from a few dozen meters when the birds are feeding to a few hundred meters when food is being stockpiled [START_REF] Vittoz | Seed dispersal distances: a typology based on dispersal modes and plant traits[END_REF]. Analyzing the proportion of predated seeds that are still viable and hence play an active role in dispersal would therefore advance our understanding of actual dispersal along corridors. However, our results have to be interpreted within the limitations of the experimental design where we studied seed predation of only two species, P. miliaceum and F. esculentum. These species represent two seed sizes within the whole range of seed size of forest species, while seed size has been demonstrated to be an important predictor of seed removal and plant recruitment after dispersal [START_REF] Dylewski | Seed size predicts global effects of small mammal seed predation on plant recruitment[END_REF].

In addition P. miliaceum and F. esculentum are not forest-species and do not develop locally. We preferred to select these species instead of artificial seeds such as in [START_REF] Wang | Scatter-hoarding rodents select different caching habitats for seeds with different traits[END_REF] or species occurring naturally in the woodlands, in order to avoid trap colonization by natural seed rain coming from local populations of these plants. However, we cannot exclude a possible bias linked to particular seed traits that might differ from seeds dispersing naturally in this landscape.

Due to domestication, seeds might not be totally representative of forest-plant seed traits in terms of nutrient content and secondary compounds, whereas those traits are known to influence predation rate [START_REF] Gong | Post-dispersal seed predation and its relations with seed traits: a thirty-species-comparative study[END_REF] and predator behavior [START_REF] Wang | Scatter-hoarding rodents select different caching habitats for seeds with different traits[END_REF] in wooded

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habitats. Predator familiarity with the species can also be an important driver of seed removal.

Next steps would be then to validate these results using a large set of small-seeded forest species, encompassing a large range of seed nutrient content and quality.

Dispersal of woodlot plants: corridors are used as both a dispersal pathways and habitat

Distance to the woodlot had low effect on the abundance or richness of seed assemblages (but see significant effect for seed abundance of forest species and tendency for animal-dispersed plants)

trapped or on the seed removal rate. Old hedgerows may provide viable corridors for forest species because of the local quality of habitat they provide to plants and contribute to the species displacement along the corridor. Local management of hedgerows is thus of particular importance in sustaining the dispersal of forest plants. Connectivity models could be perhaps improved by taking the fine-scale heterogeneity of corridors into account. Such an approach was applied successfully by [START_REF] Betbeder | Assessing ecological habitat structure from local to landscape scales using synthetic aperture radar[END_REF], who derived an index of structure entropy from radar images to characterize the heterogeneity of the cover structure. Abundance of carabids was better predicted using this entropy index than only hedgerow density at both at local and landscape scale.

Extending these approaches to plants would be an interesting research direction.

Because in our study only half of the species were also found in the species pool occurring in woodlots, trapped species may mostly be generalist species rather than specialist forest species (See Table S1 for species list). The latter species are mostly long-lived perennials with a low reproduction capacity [START_REF] Hermy | An ecological comparison between ancient and other forest plant species of Europe, and the implications for forest conservation[END_REF][START_REF] Whigham | Ecology of woodland herbs in temperate deciduous forests[END_REF] with rare long-distance dispersal [START_REF] Cain | Seed dispersal and the holocene migration of woodland herbs[END_REF][START_REF] Vellend | Dispersal of Trillium seeds by deer: Implications for long-distance migration of forest herbs[END_REF]. Most of these specialist forest species rely on local clonal multiplication [START_REF] Kolb | Effects of Life-History Traits on Responses of Plant Species to Forest Fragmentation[END_REF]. Thus for these species, actual dispersal is still difficult to assess and may require more focused methods.

Conclusion

Our study is the first to examine along corridors the actual dispersal of plant species at the community scale. Using two complementary methods, a seed trapping and a seed removal experiment, we demonstrated the positive effect of habitat corridors on the actual dispersal of forest plant species in a fragmented agricultural landscape but highlighted the potential effect of corridors on the predation pressure. The increase of the predation pressure in corridors may partly counterbalance the positive effect of corridors on seed fluxes, or alternatively play a role in seed dispersal. This result has to be confirmed using a larger set of species, growing in forest habitats,

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and encompassing a larger range of seed traits. In addition our results suggest that what enhances seed dispersal in agricultural landscapes is also the quality of the corridor in addition to the presence of a corridor per se, Indeed the corridor structure, especially its three-dimensional morphology, could determine the movement of seeds and/or provide a suitable habitat corridor for the step-by-step migration of woodland plants from one woodlot patch to another. Overall the present study emphasizes the role of hedgerows as a supplementary habitat for plants and the importance of hedgerows in biodiversity conservation in fragmented agricultural landscapes through their additional role in plant dispersal. This particular case study on woodland species assemblages underlines therefore the importance of disentangling the effect of corridors on dispersal from the conditions prevailing in the patch where the seeds finally establish. Assessing actual dispersal along corridors should advance our understanding and analysis of the functional connectivity of forest patches.

Figure 1

 1 Figure 1 Sampling design. (a) LTSER site area ("agricultural bocage" site) with the 16 sampled woodlots linked with least cost paths. Paths were calculated within the site limit (black line) + a 3km-buffer around (b) Sampling in a given woodlot encompassing two pathways (unconnected and connected). Four sampling plots were delimited for each pathway following a regular distance to the woodlot. They were located in forested habitats. Each sampling plot consisted in 15 seed traps used for studies 1 (Trapping study) and 2 (Seed removal study).

Figure 2

 2 Figure 2 Number of sampling points for connected and unconnected pathways depending on hedgerow structure. Hedgerow structure was characterized using a six-class index.

Figure 3

 3 Figure 3 Number of trapped seeds, species richness of emerged seeds and abundance of emerged seeds (Mean and Standard Deviation) for connected and unconnected pathways. Significant differences are indicated with asterisks (Detailed statistics in table 1).

Figure 4

 4 Figure 4 Number of seeds (buckwheat and millet) removed from the 40 seeds originally placed at sampling points, according to the sampling period (October and February 2014) and date of survey (Day 2 and Day 6), for connected and unconnected pathways. Significant differences in seed removal rates per species are indicated with asterisks.

Table 2

 2 Effect of type of pathway and distance from woodlot source (15 woodlots for Buckwheat and 14 for millet) on remaining millet and buckwheat seeds. February and October experiments were performed in 2014. Given model parameters are the Chi-square statistic (Chisq) and p-valuesAccepted Articlefrom ANOVA II, the marginal and complete R² (R²m; R²c) and the AIC. Variables in bold and in bold-italics are statistically significant (p < 0.05) and tend to be significant (p<0.1) respectively .

				Pathway type	Source distance	Interaction
		R²m (R²c)	AIC	Chisq-test	p-value	Chisq-	p-value	Chisq-test	p-value
						test
	Millet
	October
	Seeds remaining	0.16	742.6	18.82	1.4 x 10 -5	0.81	0.85	3.60	0.30
	(Day 2)	(0.30)
	Seeds remaining	0.16	709.1	15.83	7.0 x 10 -5	0.60	0.90	3.96	0.26
	(Day 6)	(0.32)
	February
	Seeds remaining	0.06	824.1	1.09	0.30	2.03	0.57	3.65	0.30
	(Day 2)	(0.07)
	Seeds remaining	0.11	802.4	4.57	0.03	2.57	0.46	6.63	0.08
	(Day 6)	(0.18)
	Buckwheat
	October
	Seeds remaining	0.19	1511.6	14.44	1.4 x 10 -4	37.13	4.3 x 10 -8	9.52	0.02
	(Day 2)	(0.98)
	Seeds remaining	0.01	362.9	1.22	0.27	3.56	0.31	0.92	0.82
	(Day 6)	(0.02)
	February
	Seeds remaining	0.01	711.9	0.001	0.97	2.61	0.45	3.17	0.36
	(Day 2)	(0.01)
	Seeds remaining	-	-	-	-	-	-	-	-
	(Day 6)

Acknowledgements

We are grateful to Helene Rousseau, Guillaume Bouger, and Isabelle Picouays for their help with the technical preparation of our experiments and for assistance in the field. RapidEye imagery was provided by GEOSUD. We beneficiated from the landscape database collected by J. Nabucet (UMR LETG) for the LTSER site "ZA Armorique". We are also grateful to Daphne Goodfellow for English editing.

French Ministry of Agriculture (DIVA 3 -Agriconnect project: Continuités écologiques dans les paysages agricoles)

Data availability statement

Data are available on the figshare platform at the following private link https://figshare.com/s/a10e93dc3288f23361d3 (open access will be granted after article acceptance)

Author contributions

A.E., C.M., A.B. and F.B. conceived the research idea and all authors contributed to the methodology of the study. All authors collected the data. C.M. and A.E. performed the statistical analyses; C.M. and A.E. wrote the first draft of the paper. All authors contributed critically to interpretations of results and to the drafts and gave final approval for publication.


François Bouchaud
email: francois.bouchaud@gendarmerie.interieur.gouv.fr

Thomas Vantroys
email: thomas.vantroys@univ-lille.fr

Alexandre Boe
email: alexandre.boe@univ-lille.fr

Characterization of a Connected Object by Its Acoustic Signature

Keywords: Internet of Things, Identification, Acoustic fingerprinting, Signature, Side-channel attack

With the proliferation of connected objects, the fine identification and the technical characterization of digital devices are one challenge for the monitoring and security of information systems. Connected objects are of different types, and each has different characteristics, whether it is the embedded operating system, their sensors or their firmware version. Connected objets are difficult to identify by non-expert. The activity of a microcontroller generates a set of physical phenomena that can be quantified, observed and exploited. The acoustic emissions resulting from this activity are quantifiable and have been proven to be a formidable means of discrimination and qualification of electronic equipments. In this paper, we present our prelimary work on generating "digital fingerprint" of connected objects by exploiting the emitted acoustic emissions to automatically categorize the devices.

Introduction

With an estimated average annual growth rate of 11.3% between 2020 and 2024 by International Data Corporation (IDC) [START_REF]IDC: Worldwide internet of things forecast[END_REF], the Internet of Things (IoT) market is in full boom. This attractive sector is a significant vector of economic growth and innovation. The IoT opens up new perspectives on the interconnection of people and goods with intelligence: home automation and access control, wearable, connected health, industrial applications and network management, transportation, environmental and energy monitoring, etc. The structuring of information systems is based on connected objects, which can communicate with the outside world via gateways, but also directly with each other. The access points are generally local: at the scale of a house or a factory. This partially decentralized network is mobile and flexible, depending on the services provided. This transformation is amplified by the phenomena makers kit and Do-It-Yourself. The physical world is becoming connected through the use of communication devices. For example, STMicroelectronics has recently released an evaluation board to integrate voice interaction into connected objects using the Alexa voice service developed by Amazon. This hardware is more flexible and versatile in terms of use and configuration.

As soon as we start to depict some scenarios for studying connected objects, we realize that, given the diversity of objects and protocols used, there is no universal approach to accessing to data. It is therefore imperative to know what a connected object is and how it works in order to be able to harvest information from it and, if necessary, to decide on a strategy to capture and access this internal data. Given the diversity of devices on the market, it is impossible to know in advance how all connected objects will work. Visual recognition of devices suffers from limitations due to the absence of distinguishing features or similarity in shape. The phenomenon is amplified by the wide variety of objects, spread across various application areas, the lack of information about protocols, proprietary designs, and the misuse of innate solutions or features. As an example the energy optimization solution that, depending on the services chosen, is able to integrate an acoustic sensor to characterize a sound level and therefore a presence. The time, financial and human dimensions of the study of connected objects are added to these difficulties. To overcome this problem, it is necessary to develop fine identification techniques dedicated to connected equipments, based on known characteristics, common to these objects, and/or derivable from one object to another. These tools must be robust, reliable and economic.

This article presents a preliminary work on the identification of a connected object by studying its acoustic emissions. This solution is all the more relevant as it does not involve any interaction and/or modification of the connected objects. The measurement is carried out at the periphery of the material, without prior knowledge of the environment to be studied.

Section 2 of this article summarizes previous works in the field of acoustic emissions of an electronic device; Section 3 describes the acquisition chain of sound emissions; Section 4 presents various acoustic measurements of electronic devices; Section 5 proposes the automation of the study of the signature and identification of connected objects; Section 6 presents the application domains of the solution and discusses the uses. Finally Section 7 provides the conclusion of the paper and the next step in the research.

Related Works

Emissions from electronic equipment have long been a concern for the security and privacy science community [START_REF] Briol | Emanation: How to keep your data confidential[END_REF]. Acoustic emissions are side-channels. They betray information about the electronic system. They are also used to steal secret data or to interact with the system during offensive actions. There are many sound sources that can be exploited in a computer environment, such as listening to keyboards or printers [START_REF] Asonov | Keyboard acoustic emanations[END_REF][START_REF] Zhuang | Keyboard acoustic emanations revisited[END_REF][START_REF] Backes | Acoustic sidechannel attacks on printers[END_REF], the mechanical noise of fans, or the read/write head of a hard drive. For example, researchers have shown that acoustic emanations from dot-matrix printers contain important information about the printed text. Research has also focused on the emission of sounds from production units such as 3D printers [START_REF] Faruque | Acoustic side-channel attacks on additive manufacturing systems[END_REF], or more globally from connected factories [START_REF] Association | Cybersecurity for advanced manufacturing[END_REF][START_REF] Reznick | Manufacturing: A persistent and prime cyber attack target[END_REF]. This approach assumes that the sounds carry information about the process that can be exploited in the reverse engineering of the system, without needing to access to the original design.

There are also scientific contributions on computer noise coming from the passage of electric current in electronic components [START_REF] Genkin | Acoustic cryptanalysis[END_REF]. This emanation takes the form of perceptible vibrations in the form of a low-pitched sound or a hissing noise commonly called "coil whine", although often generated by capacitors. These acoustic emanations, usually caused by voltage regulation circuits, are correlated to the system activity, since the modern processors radically modify their power consumption according to the type of operations performed [START_REF] Shamir | Acoustic cryptanalysis: On nosy people and noisy machines[END_REF][START_REF] Lemay | Acoustic surveillance of physically unmodified pcs[END_REF]. However, the bandwidth of these signals is very low: limited to 20 kHz for audible signals measurable by conventional microphones, and up to 200 kHz with ultrasound microphones. Beyond these frequencies, the attenuation in the air and the reduced sensitivity of the microphones make the signals undetectable. The study of the signals should allow us to discriminate a device, to recognize its activity rate and to characterize it. The operations of the processors being of the order of the kHz are not observable in the state. This information is drowned in the noise of 300 kHz. The study of acoustic emissions also has industrial applications in the context of the performance of existing solutions, the quality approach and the verification of equipment.

The fingerprinting of connected objects can also be realized based on the radiofrequency communication [START_REF] Polak | Identification of wireless devices of users who actively fake their rf fingerprints with artificial data distortion[END_REF][START_REF] Klein | Application of wavelet-based rf fingerprinting to enhance wireless network security[END_REF][START_REF] Xu | Device fingerprinting in wireless networks: Challenges and opportunities[END_REF] Our goal is to generate "digital fingerprint" of connected objects in order to simplify the work of forensics investigator. We investigate the use of accoustic signature because it can be easily realized on the field. As we want to test this method during legal forensic investigation, we also need to have a method who doesn't need radio communication. We have to ensured that no interaction is done between the connected objects and the Internet.

Experimental Configuration

The measurement environment consists in a robust data acquisition and processing chain deployed in a controlled environment. This chain is based on economic and easily transportable solutions.

Measuring Chain

The specifications of the sound measurement acquisition chain are conditioned by the needs of robustness, adaptability to the environment and low cost. For the experiments, we capture the acoustic noise of various communicating devices using a microphone BEHRINGER ECM8000 (effective up to about 98 kHz, well beyond its nominal range of 20 kHz). This equipment has a very linear frequency response. For the power supply and amplification, it is connected to a STEINBERG UR12. The amplified signal of this acquisition chain is digitized at a sampling frequency of 192 kHz (Fig. 1).

Fig. 1. Acquisition chain for sound measurement

The audio signal processing is done with a Rasberry Pi. The acquisition of the measurements is based on the Library PyAudio of Python. The spectrograms of these signals are drawn using the spectrogram function of the SciPy library and by custom scripts. These developments offer a modification of the resolution of the results such as the time allocated to each fast Fourier transform (FFT) with their interleaving rate, the frequency ranges to analyze or the values to return.

Noise attenuation

In order to limit the external noises and to absorb the sound waves to avoid any reflection, an acoustic anechoic chamber has been designed (Fig. 2). This solution reproduces quasi free field conditions and absorbs the echo that can disturb the measurements. With a dimension of 560x560x310 mm, this box is covered with dihedral polymer foam. This acquisition environment is also isolated from external vibrations by an appropriate support.

The attenuation of the box has been characterized by the measurement of an external frequency variable sound source(Fig. 3). The graph represents the noise attenuation (dB) as a function of frequency (Hz). The results are consistent with the properties of the polymer used to attenuate the noise. A script is used to provide a correction to the further measurements.

The acoustic anechoic chamber embbeds only the measurement microphones. The other elements of the acquisition chain are outside in order to limit the disturbance to the measurements. A second microphone has been added to record the ambient noise. The data are subtracted from the main measurement during processing in order to cancel the punctual noise coming from outside. The Fig. 4 show the noise cancellation. The top graphs are made for a sound measurement of a charger and the bottom ones for a sound measurement of a board of STM32F769I-DISCO from STMicroelectronics. This operation makes it possible to remove a parasitic frequency around 64 kHz. We also observe an improvement of the low and medium frequencies. The series of measurements allows us to check the acoustic properties of an electronic equipment. We try to verify several hypotheses on the differentiation of materials and their behavior.

Experiments on different connected equipments

Different measurements have been done on cell phone chargers including a Motorola Moto G5 Plus (phone number 1), a Samsung Galaxy S4 mini (phone number 2), and a commercial microcontroller board STM32F769I-DISCO. This work should allow us to validate our measurement environment and to demonstrate the possibility to classify the objects by the sound emitted during the electrical charge. For this experiment, a single cell phone charger has been used to power the three devices. The cell phones have been configured as follow: standby mode, airplane mode enabled. Airplane mode is a setting that cuts off all network connections. However, this action leaves free access to softwares, and functions that do not need to be connected. We note that the sound emitted by the charger allows to differentiate between different objects (Fig. 5). We also observe that the noise from a vacuum charger is not distinctly reflected in the noise from a phone charging device or a disco card. The Disco Card noise recording contains a continuous signal around the same frequency, unlike the non-linear

Classification by activity of the device

The second experiments consist in demonstrating that the activity of device can be detected by measuring the acoustic emanations (Fig. 6). The measurements were carried out on the Motorola Moto G5 Plus (phone number 1). The cell phone were subjected to differents conditions using home made Android applications: 100 000 000 incrementations of a variable, and the calculation of the first 5000 prime numbers. These operations are triggered by pressing a button in our mobile application. We perform successive measurements with the airplane mode on, and with the airplane mode off. The launch of the computational operations is measured and symbolized by a frequency peak on the graphs. We notice a clear difference between the phases of activity during the calculations and the rest. Depending on the calculation performed, we can distinguish different periods of activity within the same block. In the case of the incrementation of a variable, we observe a rather regular signal. This instruction calls for a single type of operation. Conversely, the calculation of a prime number requires several types of operations and therefore the activation of several microprocessor circuits. This operation is carried out on a block of 1 second 80 with different phases of calculation. Disabling airplane mode reveals many frequency spikes related to network connections. This synchronization generates significant power consumption spikes. It is difficult to characterize precisely and visually a typical pattern related to a calculation operation. The multi-core processor of our phone is capable of performing several tasks in parallel, which may affect the result of our measurement. A third set of measurements was conducted to determine and qualify the activity of a device by measuring the sound emanations at the board level, and not at the cell phone charger level. Our measurements were carried out on the board STM32F769I-DISCO from STMicroelectronics. This board embeds a microcontroller STM32F769NIH6 based on the 32-bits ARM Cortex-M7 core and a LCD screen. Several screen configurations are evaluated: the home mode, the demonstration mode without animation, and the demonstration mode with animations integrating real time calculations. The results returned by our acquisition chain do not allow us to detect a particular state of the object or a characteristic pattern linked to an activity (Fig. 7). However, we notice that the recorded signals are not identical. This reading difficulty can be explained by the technical limitations of the acquisition chain equipment, mainly the limited sensitivity. For the same reasons, we were not able to further qualify the differentiation of two equipment with identical configurations (brand, models and operating system). Moreover, the electronic board emits only a weak audible and visible sound, unlike its control circuit. The laboratory equipment proposed by Genkin et al. [START_REF] Genkin | Synesthesia: Detecting screen content via remote acoustic side channels[END_REF] or a solution based on a Laser Doppler Vibrometer could provide the beginning of an answer to this question. The visual approach can also be coupled with a mathematical study for the detection of repetitive characteristic elements and the identification of significant patterns invisible to the human eye.

Automatic classification and identification

The acoustic measurements of the same cell phone charger connected to several equipments show different signal traces, allowing to discriminate them (measure 1). On the other hand, we are not able to visually characterize the activities on an electronic device (measures 2 and 3). In this section, an automatic classification of devices, based on their sound signature, is described. The classification is done in two steps: first a principal component analysis (PCA) reduction is applied, and secondly a support vector machine (SVM) classification allows to differentiate the devices.

Principal Component Analysis

Principal Component Analysis (PCA) is a descriptive method used to explore multivariate data.This mathematical approach provides a graphical representation of the information contained in a table of quantitative data. A table with n dimensions gives n principal components. These new variables correspond to a linear combination of the original variables. The objective of this method is to identify the principal components around which the variation of the data is maximal.

The mathematical approach can be modeled according to the following process (Fig. 8).

Support Vector Machine

Support vector machines or wide margin separators are a class of learning algorithms, applicable to the classification of linear data. They determine the boundary between categories from the training data. After the learning phase, they make a prediction of the category to which the encountered input belongs. This process is automatically performed. The classification of the measurements is done in Python, using the libraries numpy, sklearn, matplotlib, and custom-Spectrogram.

Automatic classification of measurements

To train the SVM, we perform a series of acoustic measurements on a Motorola Moto G5 Plus (Android 9 and ROM : AOSPExtended Pie) and a Samsung Galaxy S4 Mini (Android 6.0.1 and ROM : CyanogenMod 13). We develop an Android application that performs mathematical operations over a long period of time: increments on a variable, multiplications, and logical test (if condition). To avoid the Android system considers the loop as infinite and kills the process, some periodic pause periods were included. The learning phase involved 87 audio files and led to the creation of 8 categories. Each measurement is indicated by a colored dot

The Fig. 9 shows the PCA without SVM and with SVM as a function of several kernels: linear, radial and polynomial. The measurements taken allow us to identify each piece of equipment separately. For each equipment, we are also able to differentiate a mathematical operation. This result allows us to confirm the hypothesis of a discrimination of equipment and its operating state.

We find that no algorithm can categorize the mathematical operations of multiplication and the logical test (if condition) for the Motorola Moto G5 Plus. The measurements are grouped under one set. However, we are able to separate them visually. This may be due to the small volume of representative measurements or a software error. The linear method contains 6 measurement errors out of 87 measurements made, for an error rate of 6.9%. The radial and polynomial methods contain 4 measurement errors out of 87 measurements made, or an error rate of 4.6%.

The boundary between the categories can be refined by increasing the amount of training data. The linear method, which is less resource and computationally intensive, can be preferred.

Discussion

This sound measurement and characterization process can be used to quickly determine if a connected object complies with design recommendations. Sound measurement can be performed by a manufacturer or service provider throughout the product lifecycle: as part of a production quality audit, during a compliance test or during troubleshooting. The measurement allows to ensure that there are no modifications on the object, the correct version of firmware is embedded, or the object remains in nominal operation, in particular for maintenance and follow-up phase. This practice is all the more relevant with the intervention of many actors, in the case of outsourcing of services to benefit from a common reference of control, to proceed to random checks in any place and at any time.

The sound identification process is also relevant in the context of digital forensics. For example, connected objects encountered by forensic investigators are not always immediately recognizable, due to the lack of standardized identifiers or means to determine possible connectivity. These objects are black boxes for investigators. The identification of connected objects by an acoustic approach appears all the more interesting as this approach does not alter the object studied, due to the absence of interaction with the subject. By analyzing the different acoustic emissions of the different connected IoT devices, forensic investigators can potentially become aware of the different electronic evidence that is in the IoT devices if this electronic evidence is not directly accessible to forensic investigators. Identifying the connected object can also helps the forensic technician to extract and to exploit the evidence data. This measurement of acoustic waves can also be applied to the fight against cybercrime, and to the secure the IT infrastructure: fight against counterfeiting, attacks on data processing systems, corruption of electronic equipment, misuse of electronic material, etc. An IT department can also rely on this sound study process to validate the integration of a new connected object in the company environment.

Conclusion and future works

The field of connected objects is evolving very quickly. Research on the digital fingerprint of objects is still under development. Most existing approaches try to identify an object based on a small set of object characteristics. Often, they are only interested in hardware or software aspects, without ever confronting simultaneously these two attributes, which are inseparable in the context of the Internet of Things.

The acoustic study of electronic device gives promising results for equipment identification. With a machine learning approach, we obtain the beginning of an automatic classification of devices. The behavior of commercial cell phones can be discriminated just by listening to the acoustic noise emitted by the cell phone charger. Two different cell phones are quite easy to distinguish by looking at the spectrogram of the sound. More interesting, the distinction is made between similar behaviors, such as the incrementation of a variable or the calculation of prime numbers. This first step is very promising, and is easy to implement since it only uses off-the-self components. The characterization operation is all the more relevant, as the measurement is based on inexpensive tools and can be performed over a short period of time.

To go further, improvements need to be made to the measurement apparatus, including hardware, acquisition software, and processing chain. The sensitivity of the measurement will be enhanced by using more than one microphone to capture the sound data. With more data, the signal processing is more complex, but the noise can be limited more easily. To avoid the issues of machine learning on limited-size sets with a high specialization on non-interesting charateristics, the machine learning will be directed by some physical characteristics, manually selected. The selection process of physical based metrics is not obvious and should be explored more into the detail. Indeed, a small change in the metrics will lead to major changes in the classification process. Using some "optimized" metrics will help to define a digital fingerprint based on sound emanations. The fingerprints can be stored in databases. The use of such metrics could be also used to refine digital traces in order to fit to the uses. For example in forensic studies, the metrics could be selected in order to extract specific features from the measurements, such as trying to find counterfeit devices or discriminating between objects with a high processing activity from others with a limited activity.

Fig. 2 .

 2 Fig. 2. Modeling of acoustic anechoic chamber

Fig. 3 .Fig. 4 .

 34 Fig. 3. Frequency characterization of the acoustic insulation of the box

Fig. 5 .

 5 Fig. 5. Spectrograms (from top left to bottom right) of the empty chamber, the cell phone charger alone (not plugged in), microcontroller board, cell phone 1, and cell phone 2

Fig. 6 .

 6 Fig.6. Spectrograms (from top left to bottom right) of the phone 1 in quiet conditions and airplane mode enabled; variable incrementation and airplane mode enabled; quiet conditions and airplane mode disabled; calculation of prime numbers and airplane mode enabled

Fig. 7 .

 7 Fig. 7. Spectrogram of the sound emission of a Disco board with LCD screen displaying the home screen (top left), demo screen 1 (top right), and demo screen 2 with real time effects (bottom)

Fig. 8 .

 8 Fig. 8. Principal Component Analysis

Fig. 9 .

 9 Fig. 9. Categorization by PCA and SVM


Jordan Nowak

Philippe Fraisse

Andrea Cherubini

Jean-Pierre Daures

Assistance to Older Adults with Comfortable Robot-to-Human Handovers

Todays' ageing and increasingly dependent population has strong needs in terms of assistance. In this article, we propose the design of comfortable robot-to-human handovers, guaranteeing manipulability and limiting the effects of gravity forces at the transfer point. Our framework relies on vision and on a bio-mechanical model of the human arms, to obtain a handover area adapted to the person. We validate our developments with the Pepper robot handing over objects to elderly people in a nursing home. The results show that the robot's movements, the distance of the exchange, and the reached zone are all satisfactory, so the person can recover the object in good conditions.

I. INTRODUCTION

A. Motivation

The number of dependent people is increasing as the world's population ages. To maintain at home dependent older adults, and have them regain acceptable comfort in their daily lives, there are many, and increasingly so, specific tools and aids (for example, electric can and bottle openers, grab bar in the toilet or shower, etc.). With the arrival of robotics, health professionals see a possible interest in lightening their work and that of natural caregivers.

Assistive robotics is an opportunity to address this health challenge and help aging populations limit their loss of autonomy and improve their care. It has many advantages, such as simply keeping company, allowing video calls, enabling telepresence, alerting care professionals or the caregiver in case of need, or even bringing objects to the patient. In this study, we focus on physical assistance, and particularly on robot handing over an object to a human subject, in a comfortable zone. Fig. 1 shows a result of a handover during the experimentation.

B. Previous work

Handovers are very complex and humans have a strong capacity for adaptation, and this allows them to perfectly coordinate their actions with each other. To transfer this ability to a robot, it is necessary to fully understand and deconstruct this action. This exchange involves a set of cognitive and physical processes between the two individuals. Despite major advances, object transfer is still a problem studied by researchers around the world [START_REF] Ortenzi | Object handovers: a review for robotics[END_REF]. In the context of this research, we are interested in the transfer of objects from a humanoid robot (giver) to a human (receiver). The mmjp.cessero@sfr.fr exchange can be classified into two distinct phases: the pretransfer and the physical handover of the object [START_REF] Mackenzie | The grasping hand[END_REF].

Pre-transfer phase concerns the interactions and planning which take place before the physical transfer. The giver analyse the object, grasp it correctly, perceive the receiver's intention to receive it, estimate a transfer area and carry out adequate movement planning.

Communication is important because it coordinates the exchange. and improves the predictions of both partners. Indeed, the movement of the eyes [START_REF] Castiello | Understanding other people's actions: intention and attention[END_REF] or the direction of the head [START_REF] Boucher | I reach faster when I see you look: gaze effects in human-human and human-robot face-to-face cooperation[END_REF] is sufficient to define the intention. This visual interaction allows for a more natural exchange for the human receiver. Other than the gaze, the spatial and temporal contrasts can improve the fluidity of remittances [START_REF] Cakmak | Using spatial and temporal contrast for fluent robot-human hand-overs[END_REF]. It's also necessary to find the best way for grasping an object [START_REF] Lenz | Deep learning for detecting robotic grasps[END_REF], while also anticipating the way the human would recover it afterwards -by leaving the graspable parts free [START_REF] Cini | On the choice of grasp type and location when handing over an object[END_REF].

To determine a transfer zone, an estimation of the area where the object should be transferred is implicitly performed. This transfer area must be a compromise and must be easily accessible by both individuals [START_REF] Vahrenkamp | Workspace analysis for planning humanrobot interaction tasks[END_REF].

The work of Sisbot et al. [START_REF] Sisbot | A human-aware manipulation planner[END_REF] and Suay et al. [START_REF] Suay | A position generation algorithm utilizing a biomechanical model for robot-human object handover[END_REF], have worked on a movement planner specifically designed for the transfer of objects between humans and robots. To this end, they take into account the visibility, safety and physical comfort of human arm movements. To define them, the authors refer to user studies ( [START_REF] Koay | Exploratory study of a robot approaching a person in the context of handing over an object[END_REF] and [START_REF] Dautenhahn | How may I serve you? a robot companion approaching a seated person in a helping context[END_REF]), to the theory of proximity [START_REF] Hall | The hidden dimension[END_REF] and to human ergonomics (in particular on [START_REF] Katayama | Optimization principle determines human arm postures and "comfort[END_REF]). Koay et al. [START_REF] Koay | Exploratory study of a robot approaching a person in the context of handing over an object[END_REF] and Dautenhahn et al. [START_REF] Dautenhahn | How may I serve you? a robot companion approaching a seated person in a helping context[END_REF] worked on the approach that the robot should have in terms of direction and distance. Like them, we seek to generate an ergonomic transfer space by maximizing the safety and comfort for the human arm. The criteria of distance and direction are not use, but we take into account the manipulability (possibility of movement) and the effect of gravity on the arm joints.

We draw inspiration from Pyo et al. [START_REF] Pyo | Motion planning for fetch-and-give task using wagon and service robot[END_REF], which designed a system -relying on a trolley and robot -for handing over objects to patients in hospitals. The particularity in their paper is that it calculates the manipulability index in order to give the object in the best possible condition (by pooling the manipulability map of the robot and patient). Their work ensures maximum manipulability for the human and robot.

Regarding the planning and movement, humans prefer when the movements are natural, fluent and similar to theirs. It allows them to have good legibility [START_REF] Cakmak | Human preferences for robot-human hand-over configurations[END_REF]. To have this characteristic, it is possible to use control inverse kinematics. Our control strategy is based on this type of inverse kinematics controller based on the Quadratic Programming (QP) architecture [START_REF] Tarbouriech | Admittance control for collaborative dual-arm manipulation[END_REF] to take into account different constraint. Moreover, to limit human stress and improve the user experience, the robot must have low speed motion [START_REF] Fujita | Assessment of operators' mental strain induced by hand-over motion of industrial robot manipulator[END_REF].

The physical handing over of the object begins at the moment of physical contact between the object and the receiver. Here, giver and receiver must analyse the exchange of gripping forces to confirm the correct transfer of the object. Indeed, studies highlights the importance of this haptic perception between a human and a robot [START_REF] Costanzo | Handover Control for Human-Robot and Robot-Robot Collaboration[END_REF]. These grip forces tells if the other individual whether s/he intends to let go or take the object and also whether s/he is holding it properly. For this study, the Pepper robot has very limited haptic capabilities. Indeed, it has no force sensor in its hands. Therefore, it cannot detect a human's grip by its means. In future work, it could be envisaged to add sensors to this robot allowing it to take into account grip forces and thus considerably improve the physical interaction during the transfer. For the moment, a tactile interaction on the tablet or a vocal communication allows to signal the good reception of the object by the human.

C. Object transmission objective

In this work, we assume that if humans have more ease in moving their upper limbs and if they spend less energy to keep them in a certain configuration during transfer, this will lead to safer and more comfortable transfers. Moreover, it is necessary to adapt to the person -who may present joint disorders -by taking into consideration his/her joint limits to define the handover points used by the robot.

First (Sect. II), we generate an ergonomic transfer space adapted to the person, for the transfer of an object from robot to human. This space introduces our main contribution in this paper which is the calculation and consideration of the severity index and potential energy minimizing the efforts on the human upper limbs. We couple it with the manipulability index, to adjust the generated zones and obtain a satisfactory object transfer space. It is explain later how the robot can estimate the human posture (Sect. III-B) in front of it, and thus determine the transfer point to be reached (Sect. III). Finally, The results obtained in an experiment with real users in a nursing home are presented Sect. IV. Another contribution is the feedback from these experiments.

II. HANDOVER WORKSPACE

The objective is to create a handover workspace around the person to give an object in a comfortable position. The first goal is to generate a set containing all points attainable by a person. Then, the sort of this point cloud allow to highlight the most comfortable points for object transfer.

A. Reachable workspace

First, a biomecanical model of the human is used to determine the position of his/her hand for each possible configuration. For this, only the upper limbs are used (arms and hands) motricity. We consider the biomechanical model of Kapandji [START_REF] Kapandji | Anatomie fonctionnelle: Physiologie de l'appareil locomoteur[END_REF] and simplify it, by considering only 3 rotations for the shoulder and 1 for the elbow (see Fig. 2). In case of joint disorders, it is necessary to know the maximum joint limits which the human can reach. The objective is to establish a personalized model so that the generated workspace is adapted to the abilities and physical characteristics of the person. We first need to know the height and weight of the person. Indeed, this allows us, according to anthropometric tables [START_REF] Hosey | Hidden Lines: Gender, Race, and the Body in Graphic Standards[END_REF] [START_REF] Diffrient | Humanscale[END_REF], to approximate the size of his/her upper limbs. The weight is needed to calculate the potential energy and the severity index in a given position (see Sect. II-B.2). Having defined the model, we can compute the pose of the hand frame in the main reference frame (between the two shoulders, noted R 0 , see Fig. 3). All the point clouds will be defined in this frame.

It is possible now to generate all points accessible by the hand, by rotating each of the four articulations. For this, the joint values are sampled from the minimum to the maximum values. Then, the Cartesian position of the hand can be determined. The 4 joint values which have generated it are recorded. All these coordinates are discretized in voxels, to approximate each point in a voxel at its center.

B. Ergonomic workspace 1) Manipulability indices:

To transfer an object to the person, the robot must guarantee the best conditions. Among these, we first focus on manipulability [START_REF] Pyo | Motion planning for fetch-and-give task using wagon and service robot[END_REF], [START_REF] Khatib | Robotics-based synthesis of human motion[END_REF], [START_REF] Engardt | Manipulability index optimization for a planar robotic arm[END_REF]. The manipulability index is a metric of the ease to change pose. Note that for an upper limb, there is redundancy for one hand position (many possible joint angles yield that hand position). But these different configurations do not allow the individual to have the same reactivity. Higher manipulability makes the person more comfortable when grasping objects. The Jacobian matrix J (which relates the hand speed ẋ to the joint angular velocities q via ẋ = J(q) q) is used to define the manipulability index:

m(q) = det(J(q)J T (q)) ∈ + (1)
If a point has m higher than a certain value (e.g. 90% of that of the maximal m), it is considered sufficiently manipulable. These points are shown in Figure 4. 2) Severity index : A second index is used to establish the best transfer space: this is the severity index, which informs about the forces exerted on the whole arm at a given pose. It is necessary to recall the dynamics of the arm: M(q)q + N(q, q) q + g(q) = Bu.

(

) 2
This equation gives the set of forces exerted on a joint during motion. It relates the inertia matrix M, the matrix of centripetal and Coriolis forces N, the gravity vector g, the joint torques u and the matrix B defining the action of the motors on the actuators. From the expression of the Lagrangian L, i.e.:

L = E c -E p (3) d dt δL δ q - δL δq = Bu, (4)
u and B are function of potential energy E p and of kinetic energy E c . We intend point cloud to be only a function of statically defined parameters. Therefore, the elements related to motion (inertial matrix M, matrix of centripetal and Coriolis forces N, and kinetic energies E c ) are irrelevant.

Setting E c = q = q = δL δ q = 0 yields:

g(q) = δE p δq = ∇ q E p (5)
The potential energy can be expressed as:

E p = g * (m 1 * Z 1 (q) + m 2 * Z 2 (q)), (6)
with g the gravity, m 1 and m 2 the masses of the arm and forearm+hand, Z 1 and Z 2 the heights of the centers of mass of the segments. The masses and centers of mass are established from Winter's model [START_REF] Winter | Biomechanics and motor control of human movement[END_REF]. The geometric model is used to know the coordinates of the center of mass and to express it as a function of the four joint values.

To obtain the minimal potential energy, the two-norm of the gravity vector is computed:

s(q) = g(q) 2 = 4 i=1 |g(q i )| 2 . ( 7
)
Yet, it is also important to know the sign of the potential energy. To minimize it, only the negative values obtained by equation ( 6) are kept. Practically, the values of s(q) at configurations with negative E p are minimized. This allows to remove points where the arm configuration would be too high (centre of mass of the arms above shoulder level). These points are uncomfortable to reach. Unlike the manipulability index, the severity index must be minimized. Taking all points with severity index below 25% of the maximum value yields the point cloud shown in Fig. 5. 3) Pooling: The main purpose of this point cloud is to determine the optimal zone for object transfer. The points that define the desired zone and that suit our expectations are obtained by keeping only the points in common previously represented in Fig. 4 and Fig. 5. It is possible to generate the transfer zone according to the human's preferred arm. Fig. 6 presents the points in blue for the right arm and the red for the left arm. Two view of the set of points obtained by increasing the manipulability index and minimizing the severity index (respectively greater than 90% and less than 25% of the maximum value) in relation to the left arm in red and to the right arm in blue.

III. HANDOVER APPLICATION

In order to realize this object transfer application, we still have a few points to address. The first one concerns the robotic grasping of the object. Then, the estimation of the human pose and the adjustment of the transfer space. Finally, the determination the transfer point and control the robot to reach this point.

A. The robotic grasp

The Pepper robot has fairly simple hands, which are not adapted to grasping. Each hand has a single motor that operates all the fingers at the same time and this does not allow it to realize precise gripping and a holding force important. For this reason, we have chosen simple objects for transfers, such as a packet of tissues or a small bottle of hydroalcoholic gel. These object can be held securely during the transfer (plastic fits well in Pepper's hands) and the robot can easily lift it. At the start of each experiment, an operator correctly places the object in the robot's hand during the retrieval phase. The robot extends its arm, opens its hand, then closes the hand to hold the object firmly, while the arm returns to the side of the body. This grasp phase is important and improving it will be addressed as future work.

B. Pose estimation

An RGB and a depth images taken at the same time are retrieved. Then, an algorithm allow to matching this two images and to obtain the colored depth image. This colored depth image is analyzed with OpenPose [START_REF] Cao | Openpose: realtime multi-person 2D pose estimation using Part Affinity Fields[END_REF] which is a library which allows to detect in real-time multi-person human body, hand, facial, and foot keypoints on images RGB. Therefore, it is possible to easily have the position of the shoulders and the reference point in the image. The depth information give the position of this point.

C. Workspace adjustment

The point cloud generated in Sect. II-B defines a zone that is comfortable for an object-to-human transfer. However, we still need to make some adjustments depending on the situation to define a consistent transfer point.

1) Pepper handover workspace : The robot has to transmit an object to a human in front of it. This human can be in different positions. Depending on the position of the human, the generated point cloud will be at different heights. We need to make sure that the robot can reach the targeted transfer point. As in Sect. II-A, the resulting point cloud is generated with the geometric model of the robot and a reference frame between the two shoulders. Then, the manipulability and severity index are calculated as in Sect. II-B. This will allow to adjust the reach zone of the robot by maximizing the manipulability and minimizing the severity index. In order to have a sufficiently large zone, we choose to keep the points whose manipulability index is higher than 25% and whose severity index is lower than 75% of their maximum value. The resulting cloud of points is represented in Fig. 7. This new space seems to be more appropriate for the robot and therefore defines two new limits. Indeed, the points of the so-called comfortable zone of the human between these height limits are kept. Set of points (transparent purple) generated for Pepper's right arm by minimizing the severity index (at 75% of its maximum value) and maximizing the manipulability index (at 25% of its maximum value). The barycenter of this point cloud is shown as a red star. Pepper is represented here in wireframe for a better visualization (black points and segments).

2) Human posture: The points located on the back of the person are removed because these points have little interest for this application. Then, it is necessary to estimate if the person is standing or sitting and to remove points below the hip if he/she is sitting. Indeed, we could see that when the person was sitting, there was a chance that the points of the generated point cloud would be confused with the position of the legs. There is therefore a risk that the robot gives the object too low (for a seated person) and touches the legs.

D. Transfer point estimation

A selection must be make in point cloud to see which point should be reach for the transfer of the object. The first idea was to take the closest point between this zone and the position of the robot's hand in order to limit the displacements of one towards the other. However, with this solution the point to be reached was generally a little too low.

To adjust this problem, it is necessary to take into account the Pepper's workspace, and to calculate the barycenter (see Fig. 7). The closest point between the human handover space and the position of this barycenter is estimated. This allows to ensure a more appropriate height for the object transfer.

E. Control to reach the target point

RKCL controller [START_REF] Tarbouriech | Admittance control for collaborative dual-arm manipulation[END_REF] is used to reach the transfer point by controlling the robot's arm while managing the mobile base. This C++ library allows to quickly set up the controller for the Pepper robot and offers the necessary tools to perform tasks in the Cartesian space. The controller takes into account the kinematic properties and joint constraints imposed by the robot and the intended application. Other parameters affect the control law itself, such as the gain parameters, or the time step of the control loop. We set the latter to 50ms to give the controller enough time to process the vision data between steps while being sufficiently reactive to perform smooth motions. This library ensure that the joint command does not violate the authorized limits in terms of position and velocity.

IV. EXPERIMENTATION

We set up an experiment which consists to generated different zone and realizing transfers from the robot to the human to evaluate the satisfaction of the receiver. The objective is to find an acceptable threshold combination for the object transfer. Older adult were selected as volunteers. They were requested to sit on a bed during the experiment.

A. Process and survey

First, the thresholds for the severity and manipulability indices are set up. The variation of these parameters may give very different transfer zones. We choose three combinations of thresholds which give the zones shown in Fig. 8 (generated with no joint disorders on its upper limbs).

For a good estimation of the preferences, we performed four transfers per generated zone which results to twelve transfer. Questionnaires are used to determine the most appreciated zone by the volunteer.

First, it is necessary to ask the weight, height and articular amplitudes limits to the volunteers. The algorithm generates the ergonomic transfer zone. The robot will first turn to be in front of the person in turning on its mobile base and lower or raise its head to center the person in the image. Then, the robot estimates the human pose, maps the cloud of points around the human and estimates the point of transfer to reach. The transfer can then begin and the robot looks in the direction of the person during the transfer. When the point is reach, Pepper then announces that the person can take the object. For a better interaction, voice recognition have added in order to warn the robot that the human hold the object. The robot to stop waiting and to open the hand gently.

B. Results and discussion

At first, we carried out this study with seven older adults aged between 86 and 99 (average 91.86 years ± 4.78 years). Three of this residents had limitations on their joints (mostly shoulders). The points cloud generated is more centred in front of the person and better adapted. For a person with no joint disorders, the values presented in Sect. II-A are kept.

Then, we included younger people to get more data. These new inclusions gave a total of twelve volunteers with age between 21 and 99 (average 68.83 years ± 30.28 years).

The results to the satisfaction questionnaire give the ranking shown in the histogram of Fig. 9 for the generated transfer zones (Fig. 8). It shows the results obtained for the seven residents of the nursing home (in orange), and the results obtained for all the participants (in blue). The subjects prefer that the object is given to them close enough. Indeed, zones 1 and 2 were globally appreciated because they were always easily accessible. For the movement of the robot did not surprise them and seems to suit them very well. For the distance, there was no surprise either, only some transmissions too far away (third zone). In the context of these demonstrations, this does not surprise them and they seem confident about the proximity of the exchange. Finally, for the question of the relevance of the study, the most concerned people (the residents) remain much more neutral towards this question and sometimes express their fears. For example the fear that the robot replaces the human, and impoverishes the human interactions already considered as too weak. They also fear that the interaction and the understanding of the robotic tool are not so simple on people with degraded cognitive functions. To evaluate or to have an idea of the ability to judge, understand the instructions, but also to memorize and to be attentive it would be interesting to have an evaluation of the cognitive profile beforehand.

Despite the encouraging results, we know that these experiments are carried out on a too small sample of people. It is necessary to carry out a larger number of tests in the near future to validate this work in a more concrete way.

V. CONCLUSION

This work allowed to carry out the handover of an object from robot to human, at a point considered optimal for humans. With two metrics we can establish a cloud of points that would allow us to map the most comfortable and manageable points around the individual. The first is the manipulability index, which allows to know if the person can easily change position in a given configuration of his/her arm. The second is the severity index, which gives the positions for which the human uses the least amount of energy to maintain or change his/her position. This work takes into account the weight, height, and joint limitations.

We had the opportunity to carry out an experiment directly in a nursing home with older adults and we could evaluate their satisfaction with the generated transfer zones. Generally, the object was always reachable. The subjects' answers show a preference for transfers in the zones closest to them. The volunteers with relevant joint limitations were satisfied with the transfer zone adaption.

We plan to continue our experiments with older adults, to obtain more data and more opinion on these transfers. We would also like to improve the algorithm by taking into account the person's field of view, better estimate his/her posture and detect when he/she is lying on the bed.

Fig. 1 .

 1 Fig. 1. Two images (with the displays associated below) showing the transfer of an object by the Pepper robot to an older adult sitting. The first column shows the robot initially, with the object held in hand. The second column shows that the point is reached and that the person can recover the object.

Fig. 2 .

 2 Fig. 2. Articular movements and their limits for each articulation of the 3 shoulder rotations and the elbow flexion.

Fig. 3 .

 3 Fig. 3. Human body model with the right arm kinematic chain, right arm frames and the main reference frame (R 0 ).

Fig. 4 .

 4 Fig. 4.Two view of the set of points with high manipulability index (greater than 90% of the maximum value) in relation to the upper limbs.

Fig. 5 .

 5 Fig. 5. Two view of the set of points obtained by minimizing the severity index (less than 25% of the maximum value) in relation to the upper limbs.

Fig. 6 .

 6 Fig. 6.Two view of the set of points obtained by increasing the manipulability index and minimizing the severity index (respectively greater than 90% and less than 25% of the maximum value) in relation to the left arm in red and to the right arm in blue.

Fig. 7 .

 7 Fig. 7.Set of points (transparent purple) generated for Pepper's right arm by minimizing the severity index (at 75% of its maximum value) and maximizing the manipulability index (at 25% of its maximum value). The barycenter of this point cloud is shown as a red star. Pepper is represented here in wireframe for a better visualization (black points and segments).

Fig. 8 .

 8 Fig.8. Set of points obtained for the second experiment by different minimization of the severity index (s(q)) and maximization of the manipulability index (m(q)). The percentage represents the location of the threshold in relation to the maximum and minimum indexes value. The reference frame R 0 represents the frame located between the two shoulders of the human.

Fig. 9 .

 9 Fig. 9. Histogram showing the scores (out of 5) for each zone generated in the second experiment: orange for the 7 residents of the nursing home and blue for all 12 participants of the experiment.

LIRMM, Univ Montpellier, CNRS, Montpellier, France. Emails: firstname.lastname@lirmm.fr

Clinique BeauSoleil, Montpellier, France. Emails:

ACKNOWLEDGMENT

We thank all participants in the user study, and the professionalism of the nursing home staff for the room accessibility and the contact with the residents.

INFORMED CONSENT AND COMMITTEE

All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee for the Protection of Persons concerned (CPP SUD mediterranée IV: Ref. 160908 NI ID RCB: 2017A 01898-45) on 19 June 2017.