Alex Boulton & Henry Tyne. (2014).

Corpus-based study of language and teacher education. In M. Bigelow & J. Ennser-Kananen (eds), The Routledge Handbook of Educational Linguistics. New York: Routledge, p. 301-312.

Historical Perspectives

Linguistic data can be analyzed in a number ways, ranging from the description of isolated or invented examples to the study of collected samples or datasets. The analysis of collections of linguistic data is generally associated with modern techniques, though there are examples of work as early as the Middle Ages with concordances of important texts (notably the Bible) to highlight how words are used in context. Later, we see various lexicographical projects during the Renaissance, and, in the late nineteenth century, the work of dialectologists who sought to gather and record spoken data in particular. This work was painstaking in the extreme, and computers made an early contribution to making the various processes involved vastly simpler, faster and more rigorous, resulting in the creation of the landmark Brown corpus in the 1950s (1 million words of written data), the Bank of English for COBUILD projects in the 1980s (evolving from 7 million words, written and spoken), the British National Corpus in the 1990s (100 million words, written data with 10% carefully collected spoken samples) and, in the field of dialectology and sociolinguistics, projects such as ALAVAL or PFC, containing only spoken data. Further evolution has led to the semi-automatic collection of texts via the Web for even larger corpora in the new century: the Corpus of Contemporary American English (425 million words, written plus transcripts of unscripted speech), the WaCKy corpora (2 billion words on line), GoogleBooks (500 billion words of scanned text). Meanwhile, further work has been conducted with new types of corpora: parallel corpora in translation, sound-aligned spoken corpora, multimedia corpora with video, and learner corpora of non-native speaker output. As corpora have become bigger, so the software has come to play a more important role, providing simpler, faster, more powerful options, without which the sheer quantity of data could quite simply not be accessed as a corpus. Even 15 years ago, downloading 50 concordance lines could take several minutes, and for common items Aston (1996: 179) advised users to "go and have lunch while you're waiting." Today thousands of texts can be processed virtually instantaneously on line (e.g. BYU corpora) for general needs, ad hoc corpora of tens of thousands of words can be generated in minutes (e.g. WebBootCat) for one-off needs, and increasingly user-friendly software can be downloaded free from the web (e.g. AntConc) as smaller corpora find increasing uses for specific local needs.

The implications of corpus-based study in language teaching are manifold and can be seen today largely in the form of work upstream in informing dictionaries and other reference resources, from the COBUILD resources to the present day. Indeed, it is barely conceivable today to undertake new work in the provision of mainstream language teaching materials which is not corpus-based in some way. Corpora have also been used in devising syllabuses derived from evidence of frequency of actual usage in context from both native and learner corpora (e.g. the English Profile project at Cambridge University). While course books and teaching materials have been slower to make full use of corpus data, a number of major new textbooks (for English at any rate) do take up the challenge (e.g. Touchstone; McCarthy et al. 2005). Such uses of corpora remain largely invisible to the end users, but teachers in university environments have been quick to see the potential for themselves and their learners, notably in the work of Tim Johns at Birmingham University who promoted the term

Core Issues and Key Findings

Allowing learners access to language corpora is alleged to have numerous advantages. Firstly, in terms of the language itself (i.e. as input for learners), there is a clear benefit in terms of both the quality and the quantity of data: the use of authentic materials for language teaching has been defended ever since the 1970s-1980s, in particular with the shift to communicative methods, and, as Chambers ( 2009) has pointed out, the possibility of using of corpora as authentic spoken materials came as a refreshing change in language teaching. Also, we see how authenticity through the quantitative approach can be addressed, i.e. the fact that specific forms or structures only occur in certain genres or discourse styles can be highlighted (see the work of Biber for English). In this way, the use of corpora has dramatically improved our knowledge of how language is actually used by different people in different contexts, genres, text types, and so on, the findings being used to inform all kinds of pedagogical materials which previously tended to be based on the author's intuitions or ad hoc collections of samples amassed over the years.

With regard to the question of learner access to language, numerous studies have demonstrated that the types of methods typically employed by corpus linguists and sociolinguists can be targeted for pedagogical purposes: they may include fieldwork, datacollection and other 'hands-on' activities such as transcription, or they may involve using a search engine or web-crawler to gather online data; they may involve using a concordancer to isolate forms or regular expressions, or simply to see how language behaves in a patterned manner. What is common to these methods is the idea that target language data is not presented to the learner as 'text'. This is important for teaching since many traditional learning materials and activities use texts, i.e. complete language samples. Here, however, learners may be attending to isolated details or forms (e.g. through transcription) or they may be learning from concordance lines, i.e. isolated samples of language extracted from their textual environments. Finally, it should be pointed out that engaging learners with data presupposes a host of attitudes and learning activities which focus on the principles of learning by doing and learning by discovery, a heuristic and often inductive process culminating in the inductive approach of DDL.

Research Approaches

Over 100 empirical studies to date have attempted to evaluate some aspect of corpus use: learner behavior (what they do with corpora), receptivity (their attitudes towards corpus use), success using corpora as a learning tool or as a reference resource (see Boulton 2010 on-line update for an overview). These studies are in the main relatively small-scale and qualitative, with an emphasis on questionnaires for collecting learner reactions to corpus use. Qualitative studies make it difficult to know how well the type of research is likely to transfer to other contexts; while quantitative ones have the advantage of ironing out individual differences, they thereby underestimate the individualization aspect as learners can each explore very different things.

There is however some quantitative analysis, especially for learning outcomes, the results of which are generally encouraging if not necessarily statistically significant in the majority of cases for reasons well known in dynamic systems theory or complex adaptive systems research: overemphasis on single variables is unlikely to produce convincing results in any area of FLT. From this perspective it is essential to avoid attributing too much importance to any individual study; the 'bigger picture' overview of dozens of studies is more representative, and here is seen as extremely positive.

Many of these papers cover similar populations and contexts for similar questions: English L2 (but some other languages including different scripts, e.g. Chinese); university contexts (but a few in secondary schools or elsewhere); relatively motivated, sophisticated learners at higher levels of proficiency (but some others); etc. This leaves open a number of new areas for greater exploration, as discussed in the following section.

New Debates

As we have seen, there has been considerable empirical research in corpus use for language learning and teaching, despite claims to the contrary. However, the majority of these have similar focus -questionnaires to collect learners' representations of using corpora or tests of short-term learning outcomes on specific language points for university students needing English. There is certainly a dearth of empirical research in other areas, notably:

• New contexts: How does DDL work in other contexts -in secondary and even primary education, with adults in continuing education, in private language schools or selfaccess centers? Given that the overwhelming majority of corpus-based studies are concerned with English and ELT, is it possible to teach languages other than English using corpus-based techniques (for technical, cultural, linguistic reasons); can corpusbased teaching be used by teachers of languages where teaching materials are lacking or considered to be unsatisfactory, etc.? • Longitudinal studies: How does DDL fare in the long term, not just on specific language items covered but on language as a whole? What use is made of corpora outside class or after the end of the course? Do learners apply it to other areas of study in addition to language learning, such as in information retrieval, literary or cultural studies, translation, etc.? What evidence is there that DDL really does promote autonomy, life-long learning, motivation, language awareness, metacognitive skills, learner-centeredness, etc. -all things commonly claimed in the literature but largely unexamined? How would it be possible even to go about researching these? • Tools and technology: For the purposes of DDL, can the web be considered a surrogate 'corpus', and search engines even as surrogate 'concordancers'? Can Internet searches for language purposes be seen as related to DDL, as a way in to the relative advantages of different types of corpora (written, spoken, multimodal, learner corpora, translation corpora, large / small, generic / LSP corpora, etc); are they best accessed via 'linguistic' tools (concordancers, even ones designed for pedagogical purposes), or via more comprehensive software packages (eg the Hypertext package in LexTutor); and so on. • Practice: Is DDL best presented as a radical technique, or as 'normal' practice building on existing regular classroom activities? Are corpora best seen as a learning aid per se, or as a reference resource (especially for writing and translation) alongside dictionaries and other tools? Are there any limitations to the effectiveness of DDL in different contexts, for different purposes, for different learner profiles? How can teachers be encouraged to explore DDL for themselves and for their learners in both pre-service and in-service training?

Implications for Education

Corpora have still to make substantial inroads in mainstream teaching. It has been argued that the inductive processes involved in exploring authentic language are too demanding for younger or less sophisticated users, an argument undermined by research with primary school students for L1 education (e.g. Sealey & Thompson 2004), and by work with university students with lower levels of L2 language ability (e.g. Chujo et al 2009). A more convincing explanation is perhaps that teachers lack awareness of corpora and training in their use (cf. Conrad 2000;Cobb 2009). With corpora absent from teacher training qualifications, they are inevitably underexploited in initial teacher education. Mukherjee (2004) shows how practicing teachers are receptive to the benefits of corpora for their own use, but are slower to recognize applications for their learners.

Corpora by no means contain the solutions to all teaching problems, and massive use with any group of learners is likely to be counter-productive. However, the evidence suggests that, sensitively used, they can provide an additional set of tools and techniques for a variety of purposes for at least some learners in some contexts, can increase language awareness and metacognitive skills, building on and promoting existing ICT skills, are potentially highly motivating as they allow exploration of individual questions and are thus learner-centered, fostering autonomy with potential for life-long learning. Routledge.

Additional References 2-3 most important historical books and articles

An indispensable introductory set of readings for all aspects of corpus linguistics, especially here sections 5 and 6: "Using a corpus for language pedagogy and methodology" and "Designing corpus-based materials for the language classroom".

  Johns himself was working on DDL throughout the late 1980s, butMcEnery and Wilson (1997: 2) credit even earlier work to Peter Roe in Aston University (also in Birmingham); the first published paper in the area appeared in 1980 by McKay in San Francisco. The biannual Teaching and Language Corpora conferences in Europe are testament to the continued research interest in the field, as are the number of books and articles in the area. Today, corpora are widely used in university environments, especially in translation studies and for learners needing languages for specific or academic purposes, and even for younger learners in school in a number of individual initiatives.

	Alex Boulton & Henry Tyne. (2014). Corpus-based study of language and teacher education.
	In M. Bigelow & J. Ennser-Kananen (eds), The Routledge Handbook of Educational Linguistics.
	New York: Routledge, p. 301-312.

This is a pre-publication version. For the version of record, please see https://www.routledge.com/The-Routledge-Handbook-of-Educational-Linguistics/Bigelow-Ennser-Kananen/p/book/9780415531306 (or email me at alex.boulton@atilf.fr) "data-driven learning" (Johns & King 1991).

  AlexBoulton & Henry Tyne. (2014). Corpus-based study of language and teacher education. In M. Bigelow & J. Ennser-Kananen (eds), The Routledge Handbook of Educational Linguistics. New York: Routledge, p. 301-312.

This is a pre-publication version. For the version of record, please see https://www.routledge.com/The-Routledge-Handbook-of-Educational-Linguistics/Bigelow-Ennser-Kananen/p/book/9780415531306 (or email me at alex.boulton@atilf.fr)

  DDL, or are they totally different -and if DDL can build on Internet search techniques, does it have a washback effect on this in promoting ICT literacy in general? What are Alex Boulton & Henry Tyne. (2014). Corpus-based study of language and teacher education. In M. Bigelow & J. Ennser-Kananen (eds), The Routledge Handbook of Educational Linguistics. New York: Routledge, p. 301-312.

This is a pre-publication version. For the version of record, please see https://www.routledge.com/The-Routledge-Handbook-of-Educational-Linguistics/Bigelow-Ennser-Kananen/p/book/9780415531306 (or email me at alex.boulton@atilf.fr)

  • Sinclair, John McHardy (ed.). 1987. Looking Up: An Account of the COBUILD Project in Lexical Computing. London: Collins, p. 104-115. Sinclair is often called the father of modern corpus linguistics, and had a strong interest in pedagogical issues; this volume covers various aspects of the issues involved in compiling, annotation and exploiting the revolutionary COBUILD Bank Of English corpus. • Johns, Tim & Philip King (eds.) 1991. Classroom Concordancing. English Language Research Journal, 4. Similarly, Johns can be called the founding father of DDL, putting corpora in the hands of learners; he had already published several papers in the field, but this collected volume is considered seminal. • Tribble, Chris & Glyn Jones. [1990] 1997. Concordances in the Classroom. 2 nd edition. Houston: Athelstan. The first attempt to show in concrete terms how corpora can actually be used for teaching / learning. 2-3 most important recent books and articles • O'Keeffe, Anne, Michael McCarthy & Ronald Carter. 2007. From Corpus to Classroom: Language Use and Language Teaching. Cambridge: Cambridge University Press. A comprehensive look at uses of language corpora with pedagogical aims in mind, though with little on DDL itself. • Reppen, Randi. 2010. Using Corpora in the Classroom. Cambridge: Cambridge University Press. One of several recent books intended to bring corpora to a wider audience, including teachers. • O'Keeffe, A. & M. McCarthy. 2010. The Routledge Handbook of Corpus Linguistics. London:

  • Thomas, James & Alex Boulton (eds). Forthcoming 2012. Input, Process and Product: Developments in Teaching and Language Corpora. Brno: Masaryk University Press. The latest volume of collected papers from the biennial Teaching and Language Corpora conferences, founded in 1994.

This is a pre-publication version. For the version of record, please see https://www.routledge.com/The-Routledge-Handbook-of-Educational-Linguistics/Bigelow-Ennser-Kananen/p/book/9780415531306 (or email me at alex.boulton@atilf.fr)


Céline Franco
email: celine.franco3@yahoo.fr

Anthony Fleury
email: anthony.fleury@imt-lille-douai.fr

Bruno Diot
email: b.diot@ids-assistance.com

Applying entropy to human center of foot pressure data to assess attention investment in balance control

Applying entropy to human center of foot pressure data to assess attention investment in balance control

Céline Franco 1 , Anthony Fleury 2 Member, IEEE, Bruno Diot 1,3 and Nicolas Vuillerme 1,4 Abstract-Assessing the amount of attention invested in the control of balance is crucial when evaluating balance abilities. The purpose of the present study was to examine the relevance of applying entropy to human center of foot pressure data to assess attention investment in balance control. To achieve this goal, young healthy adults were tested in a static postural task consisting in standing as immobile as possible with their eyes closed under normal, altered (foam) and improved (anklefoot orthosis). The center of foot pressure displacements were recorded using a force platform. Three dependent variables were computed. Results showed decreased values of velocity and displacement of Center of Pressure (CoP), indicating a less important amount of postural sway, and increased values of Sample Entropy of CoP, suggesting a less amount of attention invested in the control of bipedal posture than when the somatosensation from the foot and the ankle was normal.

Index Terms-Balance control, Attention investment, Sample entropy, Center of pressure

I. INTRODUCTION

P OSTURAL control is a tangle of interactions between multiple sensory systems: the vestibular, the somatosensory and the proprioceptive ones. All these information are integrated by the central nervous system sensors and result in an automatic regulation of the balance through different feedback loops. For young healthy individuals, maintaining quiet stance is an easy task which calls up minimal attention resources. However, in more challenging conditions such as the simultaneity of a more cognitively demanding task and/or the absence of some sensory input, postural control requires a greater attention investment to avoid falling. The former case, known as dual-task paradigm, is very common in daily living (e.g. talking while standing or walking) and was intensively investigated. In the second case, a healthy individual is expected to adapt himself and compensate unconsciously by re-weighting the different afferent information while an elderly or impaired individual is expected to need to call up additional attention resources at the expense of automaticity.

Sensory-motor and cognitive mechanisms involved in the control of balance have been studied, until recently, through the computation of conventional measures based on summary statistics of the center of foot pressure (CoP) displacements. These measures provide an overall view of the postural sway and of the muscular strategy adopted (hip-or ankle-strategy) but they do not give insights neither into the control strategy, nor the attentional demand allocated to the control of balance. Deepening the understanding of the mode of control and of its underlying mechanisms has encouraged researchers to call for more powerful and relevant techniques of analysis. Nonlinear methods turn out to be successful tools for the analysis of the dynamical structure of the CoP displacements during quiet standing (RQA [START_REF] Schmit | Dynamic patterns of postural sway in ballet dancers and track athletes[END_REF], [START_REF] Riley | Effects of visual and auditory short-term memory tasks on the spatiotemporal dynamics and variability of postural sway[END_REF], DFA, Lyapunov Exponent [START_REF] Donker | Regularity of center-of-pressure trajectories depends on the amount of attention invested in postural control[END_REF], etc.). Among these parameters, entropy is of interest for quantifying the regularity of the considered time series. One of the most appropriate method to compute entropy of such signals is Sample Entropy that was applied to various physiological time series (HRV [START_REF] Govindan | Revisiting sample entropy analysis[END_REF], gait [START_REF] Murray | A pilot study using entropy as a non-invasive assessment of running[END_REF], and noteworthy to CoP displacements during the control of upright posture [START_REF] Donker | Regularity of center-of-pressure trajectories depends on the amount of attention invested in postural control[END_REF], [START_REF] Borg | Entropy of balance -some recent results[END_REF]- [START_REF] Isableu | Regularity of center of pressure trajectories in expert gymnasts during bipedal closed-eyes quiet standing[END_REF]). Very interestingly, results of numerous recent studies, which assessed postural control when manipulating the cognitive load during the execution of the postural task (through the concurrent performance of a cognitively demanding task, for instance), suggest that SEn CoP could reflect attention investment: the less attention-demanding the task, the greater entropy. That way, healthy individuals and experts are expected to exhibit greater entropy synonymous with automaticity and adaptability. Smaller values of entropy, reflecting regularity, are in turn associated with movements confined to repetitive patterns without possibility to adapt oneself to new challenges or situations as observed among the elderly or disabled people. However, the interpretation of SEn CoP is not always straightforward [START_REF] Borg | Entropy of balance -some recent results[END_REF] and may be gained from completing the analysis with more traditional measures quantifying the postural sway.

The purpose of the present study is to examine the relevance of applying entropy to human center of foot pressure data to assess attention investment in balance control. We hypothesized that (1) altering somatosensory inputs from the foot and the ankle would increase CoP fluctuation and decrease CoP entropy, whereas (2) facilitating somatosensory inputs from the foot and the ankle would decrease CoP fluctuation and increase sample entropy of the CoP. To our knowledge, although the effects of foam support on the control of bipedal posture has already been assessed in terms of CoP regularity/predictability using sample entropy of the CoP parameters (e.g. in [START_REF] Borg | Entropy of balance -some recent results[END_REF]) no study has evaluated, in the same experimental procedure, whether and how experimentally degrading, but also improving somatosensory inputs from the foot and ankle modifies attention investment in balance control, as assessed by sample entropy of the CoP.

After this introduction and state-of-the-art, the paper is divided as follows. In section II, the two experimental protocols are described and the method used for data analysis is detailed. Finally, results are presented in section III and discussed in section IV.

II. MATERIAL AND METHODS

A. Subjects

Ten young male, university students, participated in Experiment 1. Ten other young male, university students, took part in Experiment 2. They gave their informed consent to the experimental procedure as required by the Helsinki declaration (1964), and were naive as to the purpose of the experiment. Subjects included in the study had to demonstrate neither history of motor problem, neurological disease nor vestibular impairment.

B. Experimental procedure

With eyes closed, subjects stood barefoot on the force platform in a standardized position (feet abducted at 30°, heels separation 3 cm), arms hanging loosely by their sides and were asked to stand as still as possible.

In experiment 1, the postural task was executed in two support surfaces conditions (Firm and Foam). The force platform served as the Firm support surface. In the Foam condition, a 2-cm thick foam support surface, altering the quality and/or quantity of somatosensory information at the plantar sole and the ankle, was placed under the subjects' feet.

In experiment 2, the postural task was executed in two No-AFO and AFO condition. In the AFO condition, subjects were asked to wear an AFO (Thuasne, Levallois-Perret, France) on each foot.

The order of presentation of the two Firm and Foam support conditions (Experiment 1) and the two conditions of No-AFO and AFO (Experiment 2) was randomized over subjects. For each condition, subjects performed three 32s trials.

C. Measurements and data analysis

A force platform (Equi+, model PF01, Aix les Bains, France), consisted of an aluminum plate (80 cm each side) laying on three uni-axial load cells (0-50 daN), was used to measure the displacements of the CoP. Signals from the force platform were sampled at 64 Hz. Given that the effect of filtering on non-linear analysis is still debated, data were not filtered in this study [START_REF] Ramdani | On the use of sample entropy to analyze human postural sway data[END_REF]. The antero-posterior and mediolateral CoP time series were centered on zero mean prior to constructing the resultant distance (RD) CoP time series. Specifically, RD is the vector distance from the center of the posturogram to each pair of points in the antero-posterior and medio-lateral time series.

On the one hand, to assess the postural performance, we quantified the amount and the control of postural sway by calculating:

1) The root mean square of the CoP displacements

A CoP = 1 N N i=1 RD 2 i (in mm) 2)
The root mean square of the CoP velocities

V CoP = 1 N -1 N -1 i=1 ( RDi+1-RDi δt ) 2 (in mm/s)
where δt = 0.016s and N is the number of samples. On the other hand, to give insights into the dynamical structure and particularly into the regularity/predictability of the CoP trajectories, we calculated the sample entropy of the RD time series as follows [START_REF] Ramdani | On the use of sample entropy to analyze human postural sway data[END_REF], [START_REF] Richman | Physiological time-series analysis using approximate entropy and sample entropy[END_REF]:

1) The time series was normalized (zero mean and unit variance) to make the measure size-and scaleindependent. 2) mand (m + 1)dimensional embedding vectors were constructed with a time delay equal to 1 i.e. with consecutive points:

z k = (RD k , RD k+1 , ..., RD k+m-1 ) for 1 ≤ k ≤ N -m and w k = (RD k , RD k+1 , ..., RD k+m-1 , RD k+m ) for 1 ≤ k ≤ N -m -1. 3) For 1 ≤ i ≤ N -m, the probability B m
r (i) that another m-dimensional embedding vector is close to z i with a tolerance of r% of the standard deviation (SD) of the time series is calculated along with their mean

B m r = 1 N -m N -m i=1 B m r (i): B m r (i) = 1 N -m+1 N -m j=1, j =i Ω(r.SD -max 0≤k≤m-1 |RD j+k - RD i+k |)
where Ω is the Heaviside function. In other words, B m r is the probability that two different sequences match for m points. In the same way, A m r (i) are calculated for the (m + 1)-dimensional embedding vectors w i . and A m r is the probability that two different sequences match for (m + 1) points. 4) Finally, for N large enough SEn CoP (m, r, N ) = -log(CP ) where CP = A m r /B m r . It is the negative natural logarithm of the conditional probability that a sequence of data points with length N , having repeated itself within a tolerance r for m points, will also repeat itself for (m + 1) points, without allowing self-matches [START_REF] Richman | Physiological time-series analysis using approximate entropy and sample entropy[END_REF]. Parameters values of m (m = 3) and r (r = 0.05) were based on the optimization procedure described by Ramdani [START_REF] Ramdani | On the use of sample entropy to analyze human postural sway data[END_REF]. Indeed, SEn CoP was calculated for many couples (m, r), with m ranging from 1 to 6 and r from 0.05 to 0.5. A subset of m was extracted by observing a pseudo-convergence criterion of evolution of the median sample entropy depending on r for a given m. Then, the r minimizing, the maximum relative error of the entropy estimation and of the CP estimate, Q(m, r) was chosen. This last quantity is defined by: Q(m, r) = max( σ CP (m,r) CP (m,r) , σ CP (m,r)

-log(CP (m,r))CP (m,r) ) where σ CP is an estimate of the variability in the conditional probability CP .

D. Statistical analysis

The means of the three trials performed in the two experimental conditions were used for statistical analyses. A Kolmogorov-Smirnov test of equality of variances first showed that the distributions used for the analysis did not depart from normality (p s > 0.05). For experiment 1 (respectively, experiment 2), data obtained for the Firm and Foam conditions (respectively, No-AFO and AFO conditions) were compared using two-tailed t-tests. Level of significance was set at 0.05.

III. RESULTS

A. Experiment 1

Analysis of the A CoP showed smaller values in the Firm than Foam condition (t = -5.07, p < 0.001, Fig. 1a). Analysis of the V CoP showed smaller values in the Firm than Foam condition (t = -6.54, p < 0.001, Fig. 1b). Analysis of the SEn CoP showed larger values in the Firm than Foam condition (t = 3.18, p < 0.05, Fig. 1c).

B. Experiment 2

Analysis of the A CoP showed larger values in the no-AFO than AFO condition (t = 4.70, p < 0.01, Fig. 2a). Analysis of the V CoP showed larger values in the no-AFO than AFO condition (t = 3.79, p < 0.01, Fig. 2b). Analysis of the SEn CoP showed smaller values in no-AFO than AFO condition (t = -3.13, p < 0.05, Fig. 2c).

IV. DISCUSSION AND CONCLUSION

Maintaining quiet standing is as well-experienced and rather automatic task as it may become very difficult from the moment that we are deprived of the complete use of our senses or that the environmental constraints are unusual or unexpected. Such circumstances of degraded postural control are notably observed within the elderly and are the primary cause of fall and related injuries. Looking for relevant and easy-to-use conditions of postural control assessment to give insight into attention investment in balance control is of great importance.

To assess the relevance of applying entropy to center of foot pressure to infer attention investment, young healthy adults were tested in a static postural task with their eyes closed. We remove visual information as it was previously shown to substantially reduce the postural effect caused by the manipulation of somatosensory information from the foot Concerning the assessment of postural control for attention investment, [START_REF] Schmit | Dynamic patterns of postural sway in ballet dancers and track athletes[END_REF], [START_REF] Riley | Effects of visual and auditory short-term memory tasks on the spatiotemporal dynamics and variability of postural sway[END_REF] assessed the control of posture during quiet standing in foam condition with RQA analysis of the CoP and found opposite results of entropy from ours. However, their measure of entropy is different from our and the closest parameter is the percentage of recurrence which increased in the foam condition for Schmidt indicating a more regular time series but decreased in the foam condition for Riley.

SEn CoP was used to investigate the effect of support surface on the control of upright posture and was found to decrease in the foam condition in Stins et al. [START_REF] Stins | Sway regularity reflects attentional involvement in postural control: Effects of expertise, vision and cognition[END_REF] and Strang et al. [START_REF] Strang | Structural changes in postural sway lend insight into effects of balance training, vision, and support surface on postural control in a healthy population[END_REF], but no significant effect was observed by Borg and Laxaback [START_REF] Borg | Entropy of balance -some recent results[END_REF]. Discrepancies may be due to the features of foam support used: thickness and density of the foam. In our case, SEn CoP decreased in the Foam condition in line with Stins et al. [START_REF] Stins | Sway regularity reflects attentional involvement in postural control: Effects of expertise, vision and cognition[END_REF] and Strang et al. [START_REF] Strang | Structural changes in postural sway lend insight into effects of balance training, vision, and support surface on postural control in a healthy population[END_REF] and increased in the AFO condition in comparison with the control one. This means that the dynamical structure of the CoP displacements is more irregular with the AFO and more regular on the foam. In other words, the trajectories of the CoP are richer and unpredictable in the enhanced somatosensory condition from the foot and the ankle, whereas same patterns tend to repeat themselves in the degraded somatosensory condition from the foot and the ankle. In terms of control, this means that the AFO condition is less attention-demanding and is more automatic than the Foam condition is (Fig. 3). These results about the attentional investment and automaticity are in line other results [START_REF] Borg | Entropy of balance -some recent results[END_REF], [START_REF] Roerdink | Center-of-pressure regularity as a marker for attentional investment in postural control: A comparison between sitting and standing postures[END_REF]. The interpretation of entropy is not always straightforward because it depends on the attention invested, automaticity and noise. However, this difficulty may be overcame by combining SEn CoP parameter with conventional measures. In this way, greater values of entropy associated with smaller postural sway may be interpreted as a complex fine tuned control of upright posture (Fig. 3). Finally, a shift in mode of postural control occurred with somatosensory modulation and SEn CoP turned out to be a relevant tool to detect and quantify its extent.

To conclude, taken together, results from experiment 1 and from experiment 2 lend supports the use of sample entropy as a suitable straightforward tool to assess the degree of automaticity in control of balance. These results are all the more encouraging that such monitoring and simple assessment of postural control may be easy to reproduce in a smart home environment by its integration into different acquisition systems embedded in several objects of daily living.

Fig. 1

 1 Fig. 1 Mean and standard error of mean of the variability (ACoP ) (panel A), the velocity (VCoP ) (panel B), and the regularity (SEnCoP ) (panel C) of center of foot pressure (CoP) trajectories obtained in the two conditions Firm and Foam conditions. These two experimental conditions are presented with different symbols: Firm (white bars) and Foam (black bars). The significant p-values for comparisons between Firm and Foam conditions also are reported (*: p < 0.05; ***: p < 0.001).

Fig. 2

 2 Fig. 2 Mean and standard error of mean of the variability (ACoP ) (panel a), the velocity (VCoP ) (panel b), and the regularity (SEnCoP ) (panel c) of center of foot pressure (CoP) trajectories obtained in the two conditions No-AFO and AFO conditions. These two experimental conditions are presented with different symbols: No-AFO (white bars) and AFO (black bars). The significant p-values for comparisons between No-AFO and AFO conditions also are reported (*: p < 0.05; **: p < 0.01).

Fig. 3

 3 Fig. 3 Schematic overview drawing a parallel between the modulation of the somatosensory information (Panel a), the CoP regularity, automaticity of control and attention investment assessed with SEnCoP (Panel b) and the postural sway assessed with ACoP and VCoP (Panel c). Improving the somatosensory context led to more irregular/unpredictable CoP trajectories, suggesting with less attention investment and greater automaticity. These results came with a decrease of the amount of postural sway highlighted by weak values of ACoP and VCoP .

*This work was funded in part by IDS company, French National Research Agency in the framework of the "Investissements d'avenir" program ANR-15-IDEX-02 and Institut Universitaire de France.


Anthony Fleury
email: anthony.fleury@imt-lille-douai.fr

Member, IEEE Quentin Mourcou
email: quentin.mourcou@wegoto.eu

Céline Franco
email: celine.franco3@yahoo.fr

Bruno Diot
email: b.diot@ids-assistance.com

Nicolas Vuillerme
email: nicolas.vuillerme@univ-grenoble-alpes.fr

Assessment of attention demand for balance control using a Smartphone: implementation and evaluation

Keywords: Attention demand, Balance, Smartphone, Wearable device, Ambient Assisted Living

Dual-task paradigm studies strongly highlights the importance of considering attention demand when assessing the ability of an individual to control balance. This paper introduces the implementation of a Smartphone application for quantitative and independent assessment of attention demand for balance control. A proof-of-concept study was designed to evaluate the effectiveness of the iBalance system in assessing the attention demand for balance control. Eight young healthy adults voluntarily performed a dual-task paradigm procedure, in which they were asked to respond vocally as rapidly as possible to an unpredictable auditory stimulus while maintaining a stable seated posture and two standing postures of increasing difficulty: bipedal and unipedal. Trunk sway measurements were used as an index of postural performance, whereas reaction time measurements were used as an index of the attention demand allocated for executing the postural tasks. In line with the existing literature, results showed that, as the postural task increased in difficulty, trunk sway and attention demand used for controlling balance increased. Taken together, these results are promising, suggesting that the iBalance system could constitute a wireless, portable, lightweight, pervasive, low-cost, user-friendly Smartphone-based system for quantitative and independent assessment of attention demand for balance control suitable for home use.

I. INTRODUCTION

A LTHOUGH it is known for decades that the control of balance involves the integration of sensory information from various sources including visual, somatosensory and vestibular systems (e.g., [START_REF] Nashner | Adaptation to altered support and visual conditions during stance: patients with vestibular deficits[END_REF]), more recently, there has been growing interest in whether and how cognitive factors could also play a role. In particular, the use of the so-called "dualtask" technique has proved extremely fruitful in understanding the attention demand of balance control.

The dual-task paradigm is a procedure in experimental (neuro)psychology that requires a participant to execute two tasks simultaneously. This technique is central to several information processing models proposing that the nervous system has a limited central capacity. It is assumed that performing a task requires a given portion of this capacity, and that if two tasks performed simultaneously require more than the total capacity, the performance of one or both tasks will be affected negatively [START_REF] Kahneman | Attention and effort[END_REF], [START_REF] Wickens | Varieties of Attention[END_REF]. When assessing attention demand for balance control, (1) the first task, called "primary", is the one for which we want to assess the attention requirement, and usually requires the control of upright posture; (2) the second task, called "secondary", is used as an indicator of the attention demand used for the execution of the primary postural task, and usually requires a rapid verbal response to an unpredictable auditory stimulus (probe-reaction time (RT) task); (3) the performance on the secondary probe-RT task is inversely proportional to the attention demand of the primary postural task.

In general, the existing literature has reported that balance control, although mainly performed at an unconscious level, is not a fully automatic process. Indeed, even the control of a bipedal stance requires attention resources, as can be indicated by the increased RTs measured in individuals standing compared with those measured when they are sitting with support (e.g., [START_REF] Lajoie | Attentional demands for static and dynamic equilibrium[END_REF], [START_REF] Vuillerme | Attentional demands and postural sway: the effect of the calf muscles fatigue[END_REF]). Furthermore, the amount of attention allocated to balance control has been shown to vary as a function of the complexity of the postural task being performed [START_REF] Lajoie | Attentional demands for static and dynamic equilibrium[END_REF], the sensory context/environment [START_REF] Shumway-Cook | Attentional demands and postural control: the effect of sensory context[END_REF], the neuromuscular constraints acting on the performer [START_REF] Vuillerme | Attentional demands and postural sway: the effect of the calf muscles fatigue[END_REF], and the performer's level of expertise. What is more, a common reported observation is that older adults and disabled people (e.g. [START_REF] Shumway-Cook | Attentional demands and postural control: the effect of sensory context[END_REF]) dedicate more attention resources for controlling their balance than do control individuals.

These results suggest that central processing factors are an important limitation for postural control, especially in individuals showing less accurate postural capacities that could place them at a higher risk of falling and that it is very important to consider attention demand when assessing the ability of an individual to control balance. Traditionally, this assessment is mostly limited to clinical laboratory setting.

These last years, some effort has been invested into the development of wearable technologies that could meet the requirements of independent balance testing and training at home. Along these lines, we have recently developed the socalled "iBalance" system, a wireless, portable, lightweight, pervasive, low-cost, user-friendly Smartphone-based system for quantitative and independent assessment of balance.

The structure of this paper is the following. Section II describes the iBalance system architecture. Sections III and IV present the experimental procedure and results of a proofof-concept designed to evaluate the effectiveness of the iBalance system in assessing attention demand for balance control. Section V discusses the results and concludes.

II. IBALANCE SYSTEM ARCHITECTURE

The presented iBalance system [START_REF] Franco | iBalance-ABF: A smartphone-based audio-biofeedback balance system[END_REF] was developed not only to evaluate simultaneously the postural control and the associated attention demand as its predecessor but also to allow the user to design his personal balance assessment program and to enjoy it on his own without constraints of time or place. This innovating system proposes to the user an application:

(1) to quantitatively and objectively assess his balance abilities in terms of performance and attention demand; (2) to personalize his balance training program by selftuning the system parameters based on his ability/progress/needs/preferences and/or goals and automatically launch the corresponding program for independent use; (3) to track and display his progress history and allow the user to share it with allowed persons. The following sections dissect the iBalance system.

A. Postural performance measurement

Trunk movements are estimated using a Kalman Filter, a standard way to compute the angles from the three different sensors (tri-axis accelerometer, magnetometer and gyroscope) that are present in the Smartphone (iPhone 4, Apple Inc.). As described in [START_REF] Franco | iBalance-ABF: A smartphone-based audio-biofeedback balance system[END_REF], a reference frame is computed as mean value of the position at the time just before the start of each trial. This allows the user to place the Smartphone wherever he wants and prevents from measurement errors that would be linked to an wrong positioning. Then, each time we sample the IMU, the current position of the Smartphone is calculated relative to this reference.

B. Attention demand for balance control measurement

During the balance assessment, the user is equipped with earphones that include a microphone to complete the auditory vocal RT task. While continuously monitoring trunk movements, the software provides unexpected, 50 ms-long beeps at 440 Hz and measures the RT of the user, i.e. the time elapsed between the start of the sound and his vocalization. The elapsed time between the emission of the consecutive auditory beeps may be tuned by the user: random or uniformly distributed. Although a common pre-processing step consists in applying a filter bank to remove most of the noise from the microphone signal, our algorithm of detection of the user's reaction was simplified at the most for computational power purposes. This signal is sampled every 10 milliseconds. At each sample, we measure the amplitude of the analog signal from the microphone denoted as A m . From this amplitude, we estimate the power of the signal that is the integration of its square:

E(t) = log( t•f i=1 A 2 m i t•f
) where f is the frequency and t the sample number. This measure is updated every 10 ms and captured from half a second before the sound emission to estimate a baseline (noise present).

A strong increase of more than 15% of energy between two measures is interpreted as the beginning of the vocalization. Depending on the way the person starts speaking, a delay of one or two samples may happen. This detection step is limited to the two first seconds from the emission of a beep.

C. iBalance software architecture

The iBalance software architecture, illustrated in Figure 1, was designed to handle with three concomitant tasks. To do so, it was hinged on three different threads (allowing a better management of the memory, the computational power available on the device):

•

D. Automatic balance assessment

The application is designed so that the user can train and execute the requested tasks on his own. The complete balance assessment protocol can be tuned by the user and saved into the application. To design his personalized protocol, the user may choose the balance assessment inputs: postural condition(s) (seated, unipedal and bipedal), with or without randomization, number of trial per condition, duration (in second), availability of the RT measurement, availability of the posture measurement, back-up of the recording data. If the RT measurement is on, the number of emission of auditory "beep" and their apparition in time (random or uniformly distributed) may be tuned. During the protocol, an included TTS functionality formulates the selected experimental condition 30s before the beginning of each trial to let the user while he is taking place quietly and preparing for the test. Between two trials, few seconds are provided to the user for resting (45 seconds between each trials). All the resting periods and the measurement periods are described clearly to the user vocally. He is instructed of what he has to do and the system lets him enough time to adapt to the conditions required.

At the end of the balance assessment, results from both postural and cognitive tasks are gathered. Mean performances are automatically calculated for each condition and sorted. Then these results may be displayed on the Smartphone screen and possibly compared with previous sessions. They may also be communicated to outsider staff.

III. MATERIAL AND METHODS

A. Participants

In a proof-of-concept study, eight young healthy adults (age: 29 ± 3 years; height: 174 ± 9 cm; weight: 66 ± 15 kg, mean ± SD) volunteered for this experiment. None of them presented any history of sensory and/or motor problems, neurological diseases or disorder that could affect their ability to perform the experiment. They gave their written informed consent to the experimental procedure as required by the Declaration of Helsinki and the local Ethics Committee after the nature of the study had been fully explained.

B. Apparatus

Participants wore the Smartphone mounted in a belt on the posterior low back at the level of L5 vertebra and a pair of earphones throughout the experiment [START_REF] Franco | iBalance-ABF: A smartphone-based audio-biofeedback balance system[END_REF]. The Smartphone was provided with a hands-free kit made of earphones with a piezoelectric microphone included.

C. Task and Procedure

We assessed the attention demand for the control of balance according to the dual-task paradigm [START_REF] Kahneman | Attention and effort[END_REF]. This paradigm consists in the simultaneous execution of two concurrent tasks: (1) the first postural task, called 'primary', is the one we would like to assess the attention demand, and (2) the second cognitively demanding task, called 'secondary', is used as an indicator of the attention demand for the execution of the primary postural task. In the present experiment, the primary task required the participants to control their balance in two upright postures of increasing difficulty: (1) bipedal standing feet parallel 10 cm apart (Bipedal posture) and ( 2) unipedal standing on their dominant leg (Unipedal posture). In the Bipedal and Unipedal postures, participants were standing with eyes closed, barefoot, arms close to the trunk, and were asked to sway as little as possible.

While performing the primary postural task, participants also performed a secondary probe-RT task. The RT task required the participants to verbally respond as quickly as possible with the word 'top' to an unpredictable auditory stimulus (50 ms, 440 Hz). The word 'top' is used as it is unrelated to the postural task and is an easily articulated 1syllable word. For each trial, 5 auditory stimuli separated by at least 2.5 s were randomly presented. Number and timing of the stimuli were similar for each condition.

Participants also were submitted to a control condition in which their RT to an auditory stimulus was assessed in a seated position. Participants were sitting normally with their back supported by the backrest of the chair. No postural measures were taken as this condition (seated) only served to establish a baseline RT value for each participant. To ensure that participants did not neglect the control of upright posture in favor of attending to the auditory stimulus, the two upright postural conditions were also performed alone without executing the RT task (five "baseline" trials for each upright posture). On the whole, five 30-s trials for each experimental condition were performed. The order of presentation of the experimental conditions was randomized.

D. Data Analysis

Two postural and one cognitive parameters are computed:

(1) the root mean square of trunk tilt in the ML (Mediolateral) and AP (Antero-Posterior) directions (RMS in degree) as a measure of sway; (2) the mean power frequency of trunk tilt in the ML and AP directions (MPF in Hz) calculated from the power spectral density of trunk tilt:

M P F = N i=1 F F Ti•fi N i=1 F F Ti
where F F T i is the value of the Fourier Transform of the angle of the trunk in the ML and AP axis at the frequency f i . (3) Reaction Time (RT) score that is defined as the temporal interval between the beginning of the presentation of the auditory stimulus ("beep") and the onset of the participant's verbal responses ("top").

E. Statistical Analysis

The means of the five trials performed in each experimental condition were used for statistical analyses. Analyses of variance (ANOVAs) were used for statistical comparison of the postural conditions (using P < 0.05).

IV. RESULTS

A. Postural data

Analyses of the trunk tilt RMS in the ML and AP directions showed significant main effects of Posture, yielding increased values in the Unipedal posture relative to the Bipedal posture (F (1, 7) = 57.18, P < 0.001, Fig. 2a, left panel and F (1, 7) = 55.76, P < 0.001, Fig. 2a, right panel, for ML trunk tilt RMS and AP trunk tilt RMS, respectively).

Analyses of the trunk tilt mean MPF in the ML and AP directions showed significant main effects of Posture, yielding increased values in the Unipedal posture relative to the Bipedal posture (F (1, 7) = 66.53, P < 0.001, Fig. 2b, left panel and F (1, 7) = 10.83, P < 0.05, Fig. 2b, right panel, for ML trunk tilt MPF and AP trunk tilt MPF, resp.).

B. Reaction time data

A prerequisite of the dual-task paradigm is to check that adding the secondary task does not affect the primary. Postural data with and without RT task were compared. For Unipedal and Bipedal conditions, there was no effect of probe introduction (P s > 0.05). This suggests that participants did not switch attention from primary to secondary task and validates the RT data as an index of the attention requirement.

To examine the attention demand of the three postural tasks, a one-way repeated-measures ANOVA with three groups (the three postures: Seated vs Bipedal vs Unipedal) was applied to RT data. Fig. 2c illustrates the means and standard errors of mean of the RT. Results showed a main effect of Posture (F (1, 14) = 24.14, P < 0.001), yielding (1) a shorter RT in while Seated than in Bipedal and Unipedal postures (P s < 0.001), and (2) a longer RT in Unipedal than in Bipedal (P < 0.05). The goal of this statistical test is to check the influence of postural task on reaction time task and so to evaluate the attention required for the first one.

V. DISCUSSION AND CONCLUSION

We present a user-friendly software, on a common and affordable device to assess balance abilities and attention demand. Following dual-task paradigm, trunk sway measurement was used as an index of postural performance, whereas RT was used as an index of the attention allocated for the performance of postural tasks. By showing longer RT in upright than in seated conditions, our results first confirmed that postural control is not fully automatic but still requires a portion of the attention resources (e.g., [START_REF] Lajoie | Attentional demands for static and dynamic equilibrium[END_REF], [START_REF] Vuillerme | Attentional demands and postural sway: the effect of the calf muscles fatigue[END_REF]). By showing longer RT in the unipedal than in the bipedal posture, our results further indicate that greater attention demand are required when the difficulty of the postural task increased [START_REF] Lajoie | Attentional demands for static and dynamic equilibrium[END_REF]. Our results are in line with the existing literature. Interestingly, contrary to what it is traditionally done, the measurements of attention demand did not require the use of any expensive, dedicated and specialized equipment, neither the help of any trained experimenter/clinician. Participants were able to efficiently and independently evaluate their abilities alone, with a Smartphone.

The software guides the user in the protocol (ability to use it on his own for auto-evaluation). It does not require any other material (than the smartphone that can be owned by the patient). Using it recurrently for assessing the evolution of the parameter of a person cost nothing more. This system can be integrated in for in-home rehabilitation or prevention and the results could be stored remotely to be accessed by medical staff integrated to its medical file.

At this point, although validation studies are needed to assess whether people showing less accurate postural capacities (e.g., elderly persons), whom the consequences of an increase in attention demand for balance control could be more dramatic, are able to efficiently handle the iBalance system for independent self balance assessment, we believe that the present results are promising. Together with studies reporting positive effects of Smartphone-based Biofeedback Systems [START_REF] Franco | iBalance-ABF: A smartphone-based audio-biofeedback balance system[END_REF], [START_REF] Lee | Cell phone based balance trainer[END_REF], the present findings suggest that it could provide utility, relevance and effectiveness for assessing and training balance abilities of person at home. More largely, we believe that the iBalance system, developed as a telerehabilitation system, could represent a suitable solution for Ambient Assisted Living technologies that could facilitate the necessary shift from intra-to extra-mural care and decrease healthcare costs.

Fig. 1 :

 1 Fig. 1: iBalance software organization with the different measuring threads.

Thread 1 :

 1 Automatic management of the global balance assessment: (a) selection of postural condition, (b) trial and rest periods management and announcement by Text-To-Speech (TTS) module, (c) postural and RT data gathering and back-up, (d) feedback to the user by displaying current results in the light of his history and finally (e) sharing the complete check-up. • Thread 2: Automatic management of the postural task: (a) computation of the reference frame, (b) data collection from the IMU, (c) calculation of body movement and trunk angulation and (d) back-up of postural data. • Thread 3: Automatic management of the RT task: (a) sound emission to the earphones, (b) data collection from microphone and (c) RT calculation/back-up.

( a )Fig. 2 :

 a2 Fig. 2: Presentation of the results for the three variables that are analyzed and for both directions of trunk tilt (Antero-posterior and Medio-lateral, considering: ***: P < 0.001 and *: P < 0.05.

*This study was supported by IDS company, the French National Research Agency in the "Investissements d'Avenir" program (ANR-15-IDEX-02) of the University Grenoble Alpes, Institut Universitaire de France and IMT Lille Douai.


Christophe Gransart

Virginie Deniau

Eric Pierre Simon

Anthony Fleury

Stéphane Lecoeuche

Emilie Masson
email: emilie.masson@railenium.eu

Patrick Millot

Éric Pierre Simon

Émilie Masson

Cyber Security of the Railway wireless system: detection, decision and Human-in-the-Loop

Keywords: cyber-attacks detection, Software Defined Radio (SDR), classification algorithms, Train-tram Simulator, scenarios of threats, analysis of driver behaviour

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The networks used in the Railway domain are usually heterogeneous, not enough protected and not fitted to the usual Cyber Security requirements in terms of sustainability, protection and attack detection. Furthermore, the quick evolution of the telecommunication means, the threats and the sustainability aspects have to be taken into account in order to protect the Railway system.

Through the SmartRaCon consortium, Railenium is an Associate Member on Innovation Programme (IP) 2 of the Joint Undertaking Shift2Rail. The activities on IP2 started through the X2Rail-1 project that deals with the start-up activities for Advanced Signalling and Automation System. In particular, Work Package (WP) 8 deals with the Cyber Security issues.

The paper presents the first contributions of Railenium on WP8 that can be divided into three main aspects dealing with the Cyber Security of the wireless part of the railway communication system: detection, decision and Human-in-the-Loop. Part of the work will be devoted to the development of an Open Pluggable Framework (OPF). The OPF is a software framework based on automation principles. It monitors the environment, then some algorithms detect abnormal behaviours, and next, OPF decides which reaction to take and finally apply this action (e.g. an alarm or a reconfiguration).

The first part of the work deals with the development of generic detection solutions able to detect different types of cyber-attacks targeting different levels of the network stack. The detection algorithms will be implemented on Software Defined Radio (SDR) cards in order to propose hardware probes monitoring the physical layer. Software probes solutions will then be developed to monitor activities on the other levels of the network stack for different network protocols. The output of these probes will be injected into the OPF which constitutes the monitoring architecture. The second part of the work consists in investigating the use of adaptive classification algorithms for the detection and identification of attacks. It would allow to: (1) Detect unknown (new) internal and external threats and intrusions, (2) Build models with incomplete knowledge about the normal and safe modes, (3) Adapt the built models to evolving behaviours of the attackers to break the security rules. In such a system, some basic rules are coded in the first initialisation of the system and then the attack detection system will monitor and analyse any kind of possible drift in the behaviour of the operator to detect and localize further attacks more precisely. The third aspect is a complement to the 2 first ones: the human remains alone in the loop to try coping with the consequences of the attack if the automatic detection and mitigation fail. Can the human replace the automatic means to detect and mitigate the threat? If so, how can we support him/her to finally secure the system or at least avoid the worst consequences?

Detection solutions of cyber-attacks

Given the rapid evolution of telecommunication and cyber threats, the railway sector has a double concern to evolve to improve its services and to protect itself in order to continue to guarantee its safety. As part of the X2Rail-1 project, Cyber Security is being considered through the design of an Open Pluggable Framework (OPF), which is responsible for managing surveillance data, decision-making functions and activation of adapted countermeasures. Work in progress is based on accurate analyses of the impacts of attacks on different communication standards. This work aims at identifying the most relevant parameters to be monitored for detecting and classifying the attacks. Different jamming attacks, with different waveforms, have to be processed in order to be able to detect the greatest number and to differentiate their respective impacts. Our approach is presented in more details in the following. We started our analysis with jammers bought on the Internet. These jammers constitute a true threat since they are cheap and very easily accessible to the general public. The first step of our analysis was the characterization of the jamming signals. This has been carried out by recording a sequence of the signal with an oscilloscope (see Fig. 1). Then, a time-frequency transform tool has been applied (see Fig. 2). It can be observed that the signal is as frequency sweeping signal, with a period (denoted in the following by the acronym SP for sweep period) of 5.5 us. Several jammers accessible to general public have been analysed and it turned out that it was always the same kind of signal. Furthermore, this result is confirmed by the outcomes of the FP7 SECRET project, where several jammers were characterized. The only difference between the jamming signals was the value of the SP, which varies from 5.5 us to 8 us behind the jammers that we tested. Then, the impact of the jamming attack on the performance of a Wi-Fi transmission 0 is investigated through measurements carried out in an anechoic chamber 0. As mentioned earlier, the nature of the jamming signal keeps the same, but the value of the SP can vary. We have then generated jamming signal in making varying their sweep period in order to investigate in detail the role of this SP parameter. In order to study the jamming signal impact on the IEEE 802.11n communication network performance, we selected an interference signal with a defined SP and we varied the Interference/Signal Ratio (ISR) by progressively increasing the interference signal power.

EM attack on a WIFI communication applying a frequency sweeping interference signal

ISR = PI/PS f 1 f 2 Sweep Period = SP
where PI is the interference jamming signal power and PS is the Wi-Fi signal power. PI and PS are obtained from oscilloscope measurements. We present, in Fig. 4, the minimum level of ISR required to loose the communication, as a function of the SP. Finally, we noticed that according to the sweep time, the interference signal power requires to corrupt the communication can vary of 30 dB. The analysis of commercial jamming was the first step. However, other threats come from the proliferation of EM signal generation equipment such as Software Defined Radio, communication shields and modules and antennas for instance. Now any jamming signal can be created with few efforts, and future standards will have to face this new threat.

Detection of attacks

To countermeasure a cyber-attack situation, the first step is to be able to detect such attack. Different detection solutions based on the monitoring of the physical link were studied in the project SECRET 0. In this TRA communication, we illustrate the monitoring approaches which can be developed on the data link layer. The monitoring approach is based on the use of the Wireshark Software. Wireshark is a network protocol analyser. It allows us to record all the traffic exchanged by the different computers using the wireless link. We obtain the frames from the Data Link layer (OSI layer 2) up to the Application layer (OSI layer 7). Currently, we are monitoring signalling frames from the IEEE 802.11n protocol and also segments from the TCP layer. By defining specific filters, we extract some information from Wireshark and we convert this information in numerical attack indicators. To illustrate the approach, the Fig. 6 represents the cumulative curve of the retransmitted frames in case of presence of jamming signal and without jamming signal.

Attack classification

From the data that have been acquired previously, some "simple" features have been extracted to check whether it is possible to find some reproducible threshold separating the space between the case of a jamming and of a normal use. It is the case of a first analysis on the number of retransmitted data as presented in the Fig. 7.

Packet numbers cumulated retransmitted frames

Only Wifi Wifi in presence of jamming However, our next goal is to find more generic models that could be able to differentiate two classes, an attack (whatever the attack) and a normal use of the network. For that, we will consider the same data as previously presented but we will extract more features from them. The general architecture of the proposed system is presented on Fig. 7. This architecture contains two main parts. A first part considers the data using a time window approach and will compute some very simple features that can easily detect some specific misuse. One of these feature is the previously presented one that will detect jamming. This case is not the worth but for some of the attacks, a real danger could appear. For these, we have to react as soon and as easily as possible (by for instance closing a connection or turning off a controller). For them we need to compute some very simple features and that do not need a large delay in the data to be evaluated correctly. The second kind of danger that will be handled are less vital attacks that could occur. For them, we propose to compute some features on the data and, using an adaptive classification method, to detect normal and attack modes in the signal. For the features that will be extracted, we propose to evaluate the capabilities of the following ones:

-Statistics on the data given by Wireshark: o Number of packets that are emitted from one source to another in a given time and their length; o FCS status of each transmissions; o Extraction from the flags; o Variability of the SNR on a duration. -Time-Frequency analysis on the raw signals: some of our past and present works focused on analysing time-frequency content of signals including the detection of the number of modes that they contain and their location in frequency, transitory effects, stationarity, regularity of predictability. This has been done using time-frequency representation based on wavelets or on Fourier transforms, and using indexes such as information entropy and their adaptation to non-stationary signals. Such indexes can easily be adapted to the signals that we acquired to detect short or long-term changes in their behaviour.

From these features, we then plan to apply machine learning techniques and specifically adaptive classification algorithms. We worked on algorithms that are able to detect some changes in the data and create new classes onthe-fly if necessary. For the beginning of the process, we will identify some attacks that we were able to reproduce in experimental conditions such as what have been described before. Then, the algorithm will analyse the data as they arrive and try to classify it as one of the classes that exist: normal or one of the known attack. If none of them is consistent with the current data, a new class will be created that will be considered by defaults as attack and will need an intervention to check.

From the data acquired, experimental results are being already processed. More data will be needed especially on the "normal behaviour" class, to perfect it and have some more interesting results.

Human approach for Risk Analysis and Threat mitigation

Principles

Our approach is focusing on "human(s) in the loop" systems and simulations, taking advantage of human ability to cope with unexpected dangerous events on one hand, and attempting to recover from human errors and system failures on the other hand. That can be achieved through a human centered automation [START_REF] Millot | Designing Human-Machine cooperation systems[END_REF]. It is based on the concepts of vulnerability and resilience [START_REF] Millot | Risk management in Life Critical Systems[END_REF]. Vulnerability "corresponds to the sensitivity of a system to threats in specific circumstances or catastrophic situations depending on:

-v1: the context influencing the system and its threats (social, political, moment in the day…) at a given time, -v2: the potential threats able to disrupt some or all the main system functions, -v3: the system functioning and its components especially those subject to degradation".

In our study, especially v2 and v3 classes of parameters will be taken into account in a vulnerability analysis. We will proceed to:

-va1: the threat characterisation (the type of event particularly malevolence), system, -va3: the environmental and internal circumstances in space and time; for malevolent threats, the political economic and social climate are important.

Mainly va1 and va2 are taken in account here, assuming that v1 as well as va3 parameters are at a high dangerous level. After this vulnerability analysis phase, we try to find preventive and/or corrective actions for reducing the vulnerability or mitigating the consequences, and so enhancing the overall system resilience.

Resilience is the "ability of a system to recover from a perturbation or a danger that could change the system state to unacceptable" [START_REF] Chatelet | Vulnerability and Resilience Assessment in Infrastructures and Networks: Concepts and Methodologies[END_REF].

The strategies are then based on risk management methods dealing with prevention, decision-making, action taking, crisis management and recovery, taking into account consequences of unexpected events, whether the origin be a technical failure, an unwilling human error or a deliberated attack [START_REF] Millot | Risk management in Life Critical Systems[END_REF] The approach globally developed in our project consists in three complementary steps: prevention, where any unexpected event could be blocked or managed before its propagation; recovery, when the event is close to result in an accident, making protective measures mandatory to avoid the occurrence of the accident; and possibly after the accident occurs, management of consequences is required to minimize damages, at least the most severe ones. Along these 3 steps and mainly the 2 first ones automatic solutions can be studied. But these automatic means can fail, and for that reason a safety and security policy generally places a series of several kinds of defenses of different and complementary natures: technical, procedural, human and organizational [START_REF] Chatelet | Vulnerability and Resilience Assessment in Infrastructures and Networks: Concepts and Methodologies[END_REF].

Technical, procedural as well as organizational defenses must have been foreseen so that the countermeasures may be played in an adequate manner. Moreover the defense based on the organization is relevant to give a collective answer and to test the pertinence of the series of defenses regarding the characteristics of the attack.

The last kind of defense we mentioned above is "human". What we mean by "human defenses" deals with all the events that have not been foreseen, and for the treatment of which no solution still exist. Therefore, the humans involved in the control and/or in the supervision of the transport system remain the last "barrier" to block or mitigate the threat if their level of Situation Awareness is high.

Situation Awareness (SA) is defined by Endsley as "the decorative term given to the level of awareness that an individual has of a situation, an operator's dynamic understanding of what is going on" [START_REF] Endsley | Towards a Theory of Situation Awareness in Dynamic Systems[END_REF]. Endsley's model is based on three levels of SA, the human ability to perceive the elements (SA1), to understand the situation (SA2) and to project that situation in the future (SA3). This concept is very useful concept for evaluating and enhancing the system resilience, either by an individual Operator [START_REF] Millot | Situation Awareness: is the glass half empty or half full? Cognition Technology & Work[END_REF] or by a team [START_REF] Millot | A common work space for a mutual enrichment of human-machine cooperation and team-situation awareness[END_REF]. Our claim is the humans can be this barrier if their Situation Awareness is increased. The present study aims to evaluate experimentally.

General Method

The method consists in 1) placing the professional human drivers and OCC supervisors in a realistic simulator, face with scenarios which would result of the consequences of a simulated cyber-attack and 2) to observe them in order to assess their abilities to react. This method is deployed in 5 steps:

1) It starts elaborating a list of threats and building related scenarios (ie. an external entity takes the control of the train, or of certain devices or the control of signals). The choice of the threats is done through an analysis of the vulnerability to cyber-attacks especially of connected and wireless devices. To be chosen the attack must have a consequence on the driving task and /or on the vehicle behavior and the driver must be able to get the relevant information to detect it and to react, either using the commands or sending an alarm to the OCC supervisor.

2) The consequent behavior of the related devices is then used as a scenario to be simulated. Devices that remain robust to cyber-attacks, for instance because of their old technology are ruled out of the list.

3) Then in the 3 rd step we simulate the scenarios involving real humans (usually professional drivers of a tram company) and analyzing the human behaviors through dedicated sensors like camera, eye movements, completed with dedicated indicators such as workload, Situation Awareness, [START_REF] Mili | Jamming Detection Methods to Protect Railway Radio Communication[END_REF] and questionnaires, in order to evaluate their abilities to detect the threats, and to give relevant answers. The scenario can be replayed of line in front of the tested human operator in order to allow the analyst asking relevant questions for further explanations on the operator behaviour, called auto-confrontation. In a variant, called allo-confrontation the analyst asks a second operator who has the same Know-how to give explanations and comments in place of the real driver on a track of his/her recorded activities. At least 12 real human operators must participate to the experiments, in order to allow the analysts comparing the different answers and detecting convergences as well as divergences in their behaviour.

4) The forth step consists in analysing these answers, the most relevant ones could be derived to establish new strategies, new procedures and/or new devices useful for human countermeasures. An important issue is the existence of procedure -or not -to respond to the threat, that impacts the human behavior in detecting the problem, understanding it, and finally finding the relevant answer. If the procedure does exist, the designer must enhance the driver Situation Awareness through, for instance dedicated displays (ie: from the detection algorithm and the automatic blocking module). If the procedure does not exist, the only answer will come from the human imagination to invent (or to adapt) a relevant countermeasure (for instance a general safety procedure as a general emergency stop). In that important case, this new solution must be transposed into new procedures and inserted in the past experience feedbacks for enhancing a training program or building a dedicated assistance tool. 5) Finally, in an ideal project, these procedures and/or devices should be integrated with new scenarios to the simulator for evaluating their utility.

Simulator PSCHITT-Rail

The Simulator PSCHITT-Rail is developed at LAMIH, lab of the University of Valenciennes. It allows 4 functionalities:

-an editor of infrastructures: railways for tram and train, with their environment (traffic, passengers on the platform, technical equipment); 45 km of railways are stored in the data base and original tracks can be developed by the software designer, -an editor of scenarios allowing to create time tables, itinerary management and events. Several scenarios corresponding to unexpected events can be stored in the data bases and played several times to several human drivers or supervisors. For instance, events occurring on the trains could result on braking defaults, wrong onboard signals… On the railway, error on rail switching control device, disturbance on the way signals, disturbances on rail-road crossing barriers can be simulated. Specific events can also be programmed by the software designer. For obvious reasons of confidentiality, we do not go further in describing the simulated attacks.

-a driving position with one driver equipped with the same devices as in a real train or tram (see Fig 8). In front of the cabin, several screens display the dynamic scenes the simulated tram is inserted in.

-a supervision work place (Central Traffic Control Position) with a human supervisor face to the same devices as in a real system: network synopsis, trains signals, rail switching … The simulator is mobile and its movements are programmed to give the cabin some realistic dynamics regarding the simulated scenario (see Fig 9).

Case studies and first results

Following the methodology defined above, the first choice was to identify a set of vulnerable devices the unusual behaviours of which could result of a cyber-attack, ie: 1) loss of the speedometer display (or display of a wrong speed value) in the cabin, 2) loss of braking, 3) jamming or disappearance of the camera rear view ) in order to study the driver answers. The list of threats to be reproduced in the simulator is established according to 4 criteria: the technical plausibility of the threat (is it technically possible?) ; the dangerousness of the threat and because we are in an exploratory study, the technical possibility to simulate the attack with our simulator. In a more systematic approach, this last criterion can be waived in enhancing the simulator. A fourth criterion is the possibility to transpose the attack to another kind of rail system, for instance from tramway to metro or to train.

As an example we describe here the result of the loss of control of the rear vision which usually allows the driver to monitor through cameras the behaviour of the passengers on the platform, especially when the tramway arrives in a station. At this moment he/she selects the side corresponding to the platform for opening the doors. That allows displaying on the screens the content of what take the front and rear cameras. Then the driver can monitor the passengers entering and going out the tram. A loss of this device can result in a serious incident or accident as reported as follows by one of the operators tested in our study who was confronted in his professional life at his driving post. He stopped his tram at a station and was going to restart after having checked the platform. But just before starting he decided to check again the rear vision, and then he detected a person who was lying on the platform and tried to catch with his hand an object under the tram. The driver said "if I started my tram at this moment, this person would get his arm cut". This anecdote shows the real interest of the rear vision for security.

The scenario starts with a normal driving during 30 minutes in a realistic and moderately loaded environment (downtown, station with several pedestrians, rail-road crossing). Then the attack appears and results in the loss of rear vision when the tram arrives at an overcrowded station with the platform on the right side of the way. The driver must then select the right side doors to be open, but due to the attack, the rear view shows an empty platform, despite a lot of passengers were visible outside on the platform and even on the pedestrian sidewalk (through the simulator screen, see fig. 10). The usual procedure to follow by the driver in that case, is first to make the situation safe and then to call the Central Traffic Control operator for asking what to do (generally disembarking all passengers of the tram and returning to the garage for maintenance). Then we would have logically expected that the different tested drivers apply this procedure, thinking to a breakdown. But none of the 6 drivers detected the display discordance (no extension of the stop time in the station, no particular comment, to him/herself or to the CTC operator, no special surprise).

During the debriefing after the experiment all drivers say they did not detect an abnormal display of the rear views. They were all focused on the driving post showing the doors, they wait they close and then restarted the tram to quit the station. This preliminary result is very instructive and surprising: -many drivers are not trained for detecting such abnormal situations, they seem to favor the direct vision instead of the devices, even these ones increase the safety; -no training sessions are proposed to the drivers to detect as well as to manage such situations, and moreover no dedicated procedures do exist.

-therefore, even the automatic countermeasures fail to prevent or to block the attacks, at least they could provide a signal in order to alert the human drivers or supervisor of the occurrence of the attack and place them in an increasing vigilance state for allowing them to react. Of course these preliminary results must be confirmed by the other cases as evoked above, but they are encouraging for developing a relevant methodology to reinforce the driver ability to detect and treat the cyberattacks, especially to define and implement relevant devices for that, that could be seen as "manual countermeasures". Moreover this methodology could be extended to several other situations for reinforcing trouble shooting procedures for the rail driving and management.

Conclusion

The first part of the paper presented our approach to investigate the impact of commercial jammers on a Wi-Fi transmission. First, the nature of the jamming signal has been identified through measurements and time-frequency analysis. The jamming signal has been modelled as a sweeping-frequency signal. Second, the jamming signal has been generated with an arbitrary signal generator, and measurements have been performed in an anechoic chamber to assess the impact of such signal on the quality of the Wi-Fi transmission. The goal of this study was to identify the most harmful interference factors on the Wi-Fi communication, with then the goal of paving the way for the design of a receiver able to adapt itself to become resilient to this kind of interference. It turned out that the relationship between the sweep period and the time-window duration of receiver signal process played a crucial role. An interesting perspective of this study would be to investigate other factors from higher network layers.

When an attack is detected it is important to deploy countermeasures as early as possible. To accelerate the deployment speed of countermeasures, the work in progress is aimed at deploying its automatically towards jamming attack. We plan to deploy an adaptive classification algorithm based on machine learning techniques to give automatic preventive countermeasures to normal or one of the known attacks and create an alert class which require an audit in case of changes in the data.

The human approach is developed to give a final answer to consequences of cyber-attacks especially if the preventive and recovery automatic countermeasures fail. In these cases, the human driver or supervisor is alone to face with the attack and we want to study experimentally the strategies he/she develops to answer to the attack. A methodology has been described involving the way to choose the more realistic attacks, to simulate their consequences in dedicated scenarios on a realistic dynamic simulator and the data to be recorded during the experiments and the expected results. An example shows the experimental results of the loss of the rear view when the tram arrives at a station and when the passengers enter and go out the train. The behaviour of 6 drivers were analysed and showed they did not detect the malfunction. We briefly discussed these first results. Of course this work is still in progress but this methodology to choose relevant cyberattacks to simulate the consequences of them seems adequate in order to draw a training program for the drivers and to enhance the procedures for fighting against cyberattacks. Moreover it seems pertinent to extend the methodology to more general trouble shouting situations in order to define new operation procedures (especially in the cases they do not exist at present). Another extension could concern new tools to assist the driver fault detection capacity and maintain their Situation Awareness.

Fig. 1

 1 Fig. 1 Characterization of the signals produced by jammers accessible to general public

Fig. 2

 2 Fig. 2 Time-frequency representation of a jamming signal

Fig. 3

 3 Fig. 3 Experimentation with a 802.11n communication in presence of jamming signal

Fig. 4

 4 Fig. 4 Required value of the ISR to interrupt the communication, as a function of the SP

Fig. 5

 5 Fig. 5 Illustration of Wireshark

Fig. 6

 6 Fig. 6 Cumulative curve of retransmitted frames We obtained a separation of the space which can permit to envisage classification works based on these retransmitted frames indicator. Other indicators are currently under study. We are notably studying an indicator based on the Frame Check Sequence (FCS) which permits to indicate if the received frame is corrupted.

Fig. 7

 7 Fig. 7 Proposed architecture for attack identification from different signals and data.

Fig 8

 8 Fig 8 Simulator PSCHITT-Rail Driver position

Fig. 9

 9 Fig. 9 Simulator PSCHITT-Rail cabin

Fig. 10 :

 10 Fig. 10: Example of rear view display (right part) incoherent with the real direct outside view (left part)

Acknowledgements

This work was performed in the framework of the WP8 (Cyber Security) of the X2Rail-1 project. The authors would like to thank the Shift2Rail Joint Undertaking for financial support. The authors would also like to thank the engineers of IRT Railenium, rHesuS consulting and LAMIH-PSCHITT-Rail for their involvement.