Lucie Steiblé
email: lucie.steible@unistra.fr

Delphine Bernhard
email: dbernhard@unistra.fr

Pronunciation Dictionaries for the Alsatian Dialects to Analyze Spelling and Phonetic Variation

Keywords: Alsatian, transcription, spelling variation

This article presents new pronunciation dictionaries for the under-resourced Alsatian dialects, spoken in north-eastern France. These dictionaries are compared with existing phonetic transcriptions of Alsatian, German and French in order to analyze the relationship between speech and writing. The Alsatian dialects do not have a standardized spelling system, despite a literary history that goes back to the beginning of the 19th century. As a consequence, writers often use their own spelling systems, more or less based on German and often with some specifically French characters. But none of these systems can be seen as fully canonical. In this paper, we present the findings of an analysis of the spelling systems used in four different Alsatian datasets, including three newly transcribed lexicons, and describe how they differ by taking the phonetic transcriptions into account. We also detail experiments with a grapheme-to-phoneme (G2P) system trained on manually transcribed data and show that the combination of both spelling and phonetic variation presents specific challenges.

Introduction

The Alsatian dialects, which belong to the High German dialects, are still spoken by approximately 500,000 speakers in Alsace, a region in north-eastern France [START_REF] Insee | Insee -Population -L'enquête "Etude de l'histoire familiale[END_REF]. Being non-dominant varieties, in a French-speaking country, they are mostly considered as oral languages, and have no standardized spelling system. There are however some occasions on which these dialects are written, dating back to 1816 when the first theater play in Alsatian, Der Pfingstmontag by Jean-Georges-Daniel Arnold, was published. Despite this written production, now stretching back to two centuries, almost no computational tools exist for the Alsatian dialects, which can therefore be considered as low-resourced. Given the absence of a standardized spelling, this article aims at investigating the relations between oral and written forms. This work was performed using lexicographic and lexical resources: for one resource, we used the transcriptions which were already provided and for the other resources we manually transcribed recordings of native speakers illustrating the lexical entries. Letter sequences and phonemes were then automatically aligned. Finally, we evaluated the challenges for a grapheme-to-phoneme (G2P) tool facing both spelling and phonetic variation. Our ultimate objective is to provide resources for the Alsatian dialects, gain a better understanding of the phonological and spelling systems and develop tools able to deal with spelling variation in text corpora. The main contributions of this article are as follows:

• We present the first pronunciation dictionaries for the Alsatian dialects, obtained by the manual transcription of several datasets ;

• We investigate phonetic and spelling variation in these datasets, with a focus on the specific status of consonants ;

• We compare various spelling systems for the Alsatian dialects, with a focus on their orthographic depth ;

• We train and evaluate a G2P system based on the manual transcriptions.

Related Work

Spelling variation is an issue for many different applications which have to process texts lacking spelling consistency (informal texts on the Web 2.0, text messaging, historical texts, dialects, etc.). While speech is rarely taken into account in traditional (written) text processing pipelines, it can be useful when dealing with spelling variation. Phonetic indexing algorithms, like Double Metaphone [START_REF] Philips | The Double Metaphone Search Algorithm[END_REF], have been developed in the context of information retrieval to account for spelling differences in words or names. The goal is to encode the input string using simplified phonetic rules. More sophisticated methods like grapheme-to-phoneme can also be used for searching words in corpora and dictionaries. 1 The goal is either to be able to retrieve words without knowing their exact spelling, or to abstract from the spelling variations in nonstandardized languages, by using a phonetic index [START_REF] Divay | Algorithms for grapheme-phoneme translation for English and French: Applications for database searches and speech synthesis[END_REF].

In addition to improving the recall of queries, phonetic indexing and grapheme-to-phoneme techniques have been used to normalize texts in non-standard spellings. For instance, [START_REF] Cook | An Unsupervised Model for Text Message Normalization[END_REF] take the phonemes of the standard word into account when normalizing graphemes in SMS text messages. [START_REF] Porta | Edit Transducers for Spelling Variation in Old Spanish[END_REF] integrate grapheme-to-phoneme transcription rules as well as rules expressing phonological change in a rule-based transducer from Old Spanish to Modern Spanish. These studies demonstrate that using knowledge about phonemes is relevant when dealing with non-standard spellings.

In order to apply a grapheme-to-phoneme system to nonstandard text a first obvious condition is to have such a system at disposal. In particular for low-resource languages, which also present high levels of spelling variation, the development of grapheme-to-phoneme systems is hindered by the lack of complete studies about the phonological system as well as the absence or the small volume of existing pronunciation dictionaries (word-pronunciation pairs). Moreover, when spelling is not normalized, designing grapheme-to-phoneme systems is known to be a complex task [START_REF] Adda-Decker | A First LVCSR System for Luxembourgish, a Low-Resourced European Language[END_REF]. The lack of resources can possibly be addressed by re-using data from other languages: [START_REF] Deri | Grapheme-to-phoneme models for (almost) any language[END_REF] present an approach to train G2P models for low-resourced languages based on word-pronunciation pairs acquired from Wiktionary and on an adaptation of high-resource languages G2P models to closely related low-resource languages. This method nevertheless requires phoneme inventories to compute a phonetic distance metric between languages.

Datasets and Preprocessing

There are five main dialectal areas in Alsace, characterized by differences in their sound inventories: Rhine Franconian, South Franconian, High Alemannic, Low Alemannic from the north of the region, and Low Alemannic from the south of the region [START_REF] Huck | L'élève dialectophone en Alsace et ses langues[END_REF]. The last two Low Alemannic variants are dominant on the Alsatian territory, and are the varieties studied here. They differ in several phonetic aspects, including the use of [C] in the south and [X] in the north after a front vowel, upholding of the [g] between vowels only in the south (in the north, the [g] became [v]) and more open vowels in final position in the south. All of them are German dialects, which creates a specific situation in this French region. According to a recent study, 43% of Alsatians can speak the dialect (OLCA / EDinstitut, 2012), and all of them also speak and write the national language, French.

The four datasets under study for the Alsatian dialects are the following:

• The DICTMULTI dataset is extracted from a printed multilingual dictionary [START_REF] Adolf | Dictionnaire comparatif multilingue: français-allemand-alsacien-anglais[END_REF]. It uses its own spelling system and provides the phonetic transcription for each word, made by the author of the dictionary. The whole dictionary is transcribed.

• The ELSASSICH dataset is taken from an online dictionary (Elsässich Web diktionnair) [START_REF] Bitsch | de Elsässich Web diktionnair[END_REF]. It uses its own spelling system, and the phonetic transcription was made manually by one of the authors of the present article, using the voice recordings provided for each lexical item. The whole online dictionary contains 3,333 entries, of which we transcribed 702.

• The OLCA datasets were produced by the local office for the preservation of Alsatian (Office pour la Langue et les Cultures d'Alsace et de Moselle, nd). It follows the ORTHAL spelling system [START_REF] Zeidler | Orthographe alsacienne: bien écrire l'alsacien de Wissembourg à Ferrette[END_REF], designed to be close to German spelling rules while at the same time preserving the variation (here between the northern part of the Alsace region -hereafter OLCA67-and the southern part -hereafter OLCA68). We used the same manual transcription process as for the ELSASSICH datasets, for a total of 2,859 entries, out of the 10,719 available.

The first dataset already had phonetic transcriptions, made by the dictionary author. Without audio files for the purpose of double checking, these transcriptions were used in their original form. The two last datasets, coming with audio, were phonetically transcribed by one of the authors, using the X-SAMPA transcription system [START_REF] Wells | Computer-coding the IPA: a proposed extension of SAMPA[END_REF].

The resulting pronunciation dictionaries are available on the Zenodo platform [START_REF] Steiblé | Pronunciation Dictionaries for the Alsatian Dialects[END_REF] along with a documentation [START_REF] Steiblé | Phonetic Transcription for the Alsatian Dialects[END_REF].

The written form was intentionally hidden during the transcription phase to avoid, as much as possible, the influence of the orthographic norms in use in the datasets. When there were doubts about some phonemes, specifically stops and their voice or voiceless quality, the waveforms were analyzed using PRAAT (Boersma and Weenink, nd). Due to the high variability of the Alsatian dialects, no complete inventory of phonemes is available. We used, though, some area-specific inventories from sociolinguistic publications to help us [START_REF] Zeidler | Orthographe alsacienne: bien écrire l'alsacien de Wissembourg à Ferrette[END_REF][START_REF] Huck | L'élève dialectophone en Alsace et ses langues[END_REF][START_REF] Bothorel-Witz | Atlas linguistique et ethnographique de l'Alsace[END_REF].

Since the Alsatian dialects are in close contact with both French and Standard German, we also used datasets in these languages to draw a comparison between them and Alsatian. Our data for German come from two sources: MaryTTS (MARY) [START_REF] Schröder | The German Textto-Speech Synthesis System MARY: A Tool for Research, Development and Teaching[END_REF], and Voxforge (VOX) (VoxForge, 2007). The French dataset is Lexique3 (LEX3) [START_REF] New | Lexique 3: Une nouvelle base de données lexicales[END_REF].

A quantitative summary of the datasets used in this study is provided in Table 1.

Language Source Entries Phonemes

Alsatian All word / phonetic transcription pairs were automatically aligned using the Phonetisaurus tool, a WFST-driven grapheme-to-phoneme framework [START_REF] Novak | Phonetisaurus: Exploring grapheme-to-phoneme conversion with joint n-gram models in the WFST framework[END_REF].

The alignment pairs link one or several letters (graphemes) with one phoneme. Figure 1 shows the alignment obtained automatically for the word Spritzkànn (watering can). This alignment process allows us to observe the consistency between sounds and graphemes, in other words the possible differences between acoustical reality and the use of a matching letter or sequences of letters.

All the analyses hereafter pertain to the 100 most frequent phoneme/grapheme pairs in each dataset (these pairs amount to 82 to 97% of all the pairs). There are 42 unique phonemes common to all the 4 Alsatian datasets. 4 These phonemes can be considered as the essential set required to describe the phonology of Alsatian dialects. They correspond to 95.6% of the total phoneme occurrences in all the datasets. The other phonemes are present in three, two or only one dataset. 5 These rarer phonemes are linked to the high variability seen in the Alsatian dialects, since some of them are allophones of the phonemes from the main list above: B (only in Low Alemannic from the south) and b, C (in Low Alemannic from the south) X, etc. Some of the variation is not related to the north/south geographical gap, but rather depends on individual variation: some speakers use R, and others r, for example. There is also a tendency found in some varieties from the south of the region to diphthong some vocalic sounds, which are very rare in other forms of Alsatian. Some phonemes are characteristic for French loanwords, such as the nasal vowels [a_~] and [o_~].

Variation at the Sublexical Level

Based on the automatic alignments between graphemes and phonemes (see Section 3.), we first investigate the orthographic depth and consistency of each spelling system and then focus on the special case of consonants.

Orthographic Depth and Consistency

There are many ways to spell sounds. In alphabetical systems, a sort of ideal could be to have a straightforward relation between phonemes and characters. This optimal situation would create a direct relation between one phoneme and one character: always the same pronunciation for one character and always the same way to write one given phoneme. Such a system could be described as having a consistent, shallow orthography. On the opposite, when it is difficult to assess the pronunciation from the spelling, or 2 a à ä b c d e f g h i j k l m n o ö p q r s t u ü v w z 3 ë é ï ì x y 4 9: 2 @ a a: ai b d e E e: E: f g h i I i: I: j k l m n N o O o: O: p r s S t u U u: v w X y y:. 5 2: 9 a_~B Ei EI o_~aI ao aU C R ts y9 Y z 3: ? @: 2I 2y 9i ei i@ ia Ia ie iE IE Oi U: Y: y9: ya when one phoneme can be written in various ways, the orthography is called deep.

Orthographic depth can be evaluated by counting the average number of pronunciations for one grapheme, and, in contrast, the average number of graphemes that can be used to spell one phoneme. These figures provide an accurate picture of the consistency between sounds and graphemes. We obtained those ratios by counting the average number of pronunciations for one given grapheme, and the number of ways to spell one given phoneme, for the 100 most frequent alignment pairs from our datasets. Of course, even when the phonetical transcriptions were made by one and the same person for the OLCA and ELSASSICH datasets, the writing system is not the same, leading to differences in the average numbers. Figure 2 displays the orthographic consistency for all our datasets, including German and French for the sake of comparison. The Alsatian dialects and German have quite similar ratios. Both can be described as having an almost "shallow orthography", or "transparent orthography". Compared to French, a language known for its complex spelling system, Alsatian is quite straightforward, despite the lack of a unified orthographic norm.

The most transparent spelling system in Alsatian is EL-SASSICH. One possible explanation could be that the authors of this specific spelling system are linguists, which could account for a more stable relation between phonemes and graphemes. While these ratios are certainly a method to evaluate orthographic depth, other experiments should be performed to assess the degree of readability and usability of each system.

Figure 2: Average number of graphemes by phoneme, per language/spelling system (plain bars), and average number of pronunciations by grapheme (hollow bars).

Consonants and "Voicing"

The status of the consonants in Alsatian is quite unclear. In dictionaries, pronunciation tables are sometimes provided, but they are contradictory, and the consonants set differs from one to another. Fricatives are usually considered as always voiceless [START_REF] Adolf | Dictionnaire comparatif multilingue: français-allemand-alsacien-anglais[END_REF] which leads to the lack of distinction between [f] and [v], for example. The categorization of plosives is less clear.

In Alsatian, all plosives are voiceless [START_REF] Steiblé | Le contrôle temporel des consonnes occlusives de l'alsacien et du français parlé en Alsace[END_REF]. Despite this, there are two categories of plosives, opposed by the feature [fortis] [START_REF] Jessen | Phonetics and Phonology of Tense and Lax Obstruents in German[END_REF][START_REF] Kohler | Phonetic Explanation in Phonology: The Feature Fortis/Lenis[END_REF]. In writing, minimal pairs can be represented, using the same characters as in French or German, such as: Gass (street) versus Kass (crate). The difference between the two sets of plosives is linked to speech events, involving two distinct temporal patterns for the production of the consonants, as shown in Figure 3. The distinction between the series is based on various cues [START_REF] Steiblé | Le contrôle temporel des consonnes occlusives de l'alsacien et du français parlé en Alsace[END_REF]. The main cues are linked to the temporal orchestration of stops production. In both absolute and relative durations, the opposition is statistically significant. The Voice Termination Time [START_REF] Agnello | Voice onset and voice termination features of stutterers[END_REF] is shorter in the fortis stops (13% of the total stop, versus 30% for the lenis stops), and the burst and Voice Onset Time [START_REF] Klatt | Voice onset time, frication, and aspiration in word-initial consonant clusters[END_REF] phase is longer for the fortis stops (25% of the total stop, versus 17% for the lenis). The silent stop gap duration is longer in the fortis stops (63% of the total stop, versus 53% for the lenis).

During the transcription by one of the authors of the EL-SASSICH, OLCA67 and OLCA68 datasets, if the consonant status was unclear, the recordings of the entries were observed on PRAAT (Boersma and Weenink, nd). If this visual check (searching for the patterns showed above) was not certain, the intra-segmental relative durations were compared to the values found in [START_REF] Steiblé | Le contrôle temporel des consonnes occlusives de l'alsacien et du français parlé en Alsace[END_REF] for fortis and lenis, respectively. Despite the acoustical and written evidence, it has been said that the Alsatian dialects do not show any distinction between the consonants (Hug, nd), probably because they differ from both German and French sounds. In fact, our dataset tends to prove that there is enough distinction between the two series to allow for a strong phonological awareness, leading to the use of the two series of letters. In German, spelling rules would warrant that a voiceless consonant be written using the matching graphemes from the voiceless list (p,t,k,c,f,s,ss,ß,ch,sch,etc). In our German datasets, however, exceptions can be found and quantified, providing us with a consistency ratio. In Alsatian, admittedly, all consonants are voiceless, but the fortis ones are written using the same letters as the German voiceless set, and the lenis ones using the complementary voiced set (b,d,g,v,z,j,etc). The consistency ratio between a consonant and the characters used to spell it amounts to 95% in French (which accounts for the clear voiced/voiceless opposition in this language). In German, the ratio reaches 90%, and Alsatian reaches 89% (over all the four datasets), despite the lack of a unified orthographic norm. It is likely that the consistency ratio found in Alsatian is not related to the variation in spelling but to it being a German dialect.

The consistency ratio of Alsatian, when measured this way, is equivalent to that of standard German, despite the lack of a unified orthographic norm in Alsatian, as shown in Figure 4.

Figure 4: Percentage of consonant graphemes matching the phonemes, in terms of phonological oppositions

G2P for the Alsatian Dialects

In this section, we use the pronunciation dictionaries to train a G2P system. This presents a double challenge: (i) the small size of the pronunciation dictionaries and (ii) the presence of both phonetic and spelling variation. Given these challenges, our goal here is not to produce a fullfledged G2P system, but to assess the performance levels which can be reached for now, both on the four Alsatian datasets and on the task of retrieving spelling variants extracted from a corpus of texts in Alsatian.

For G2P, we chose to adopt a data-driven approach and trained the Phonetisaurus [START_REF] Novak | Phonetisaurus: Exploring grapheme-to-phoneme conversion with joint n-gram models in the WFST framework[END_REF] system. The results are presented in Table 2 and are evaluated in terms of Word Error Rate (WER) -the percentage of lexicon entries with at least one error in their 1-best transcription-and Phoneme Error Rate (PER) -the Levenshtein distance between the predicted and the reference transcription, divided by the number of phonemes in the reference transcription [START_REF] Hixon | Phonemic Similarity Metrics to Compare Pronunciation Methods[END_REF]. We trained the G2P model on each dataset, then applied it to the other datasets. We also performed a closed test for each dataset by using the training data as test data. As could be expected, the results are low, given the small size of the training data. and this could account for the results obtained. We also tried to train models using combinations of the datasets but this only leads to small improvements or no improvements at all, despite the increase in the amount of training data. This might indicate that the system is not able to handle what might often correspond to contradicting cues coming from heterogeneous datasets. Finally, we applied the four Alsatian G2P models to a list of 110 words extracted from a corpus of texts in Alsatian written by several authors and corresponding to several dialectal areas and spelling systems. These words are grouped in 28 variant clusters, e.g., [Frejndschaft ; Freundschaft ; Friindschàft ; Frindschàft ; Frìndschàft] (friendship). Our goal here is to assess the ability of the G2P models to provide identical transcriptions for spelling variants. The model which performs best at this task is OLCA67, with an F-measure of 0.11. As could be expected, the precision is high (1.0), but the recall is very low (0.06), especially when compared with a rule-based Double Metaphone approach [START_REF] Bernhard | Adding Dialectal Lexicalisations to Linked Open Data Resources: the Example of Alsatian[END_REF], which is less precise (0.47) but has a much better recall (0.90). Here however the results can be improved by pooling all the datasets for training the G2P model, leading to a better F-measure (0.15) due to an increase in recall (0.08), with only a very small decrease in precision (0.97). When the task does not require phonetic realism, but rather the ability to generalize over spelling variants, a non-homogeneous training dataset seems to be a better option. One research direction could be to add known spelling variants in the pronunciation dictionaries before training.

Conclusion

In this article, we have described four word-pronunciation datasets for the Alsatian dialects, covering different spelling systems. Our analysis of the relationship between sound and spelling has shown that we observe spelling and phonetic variation between the datasets, but that the spelling systems are rather self-consistent, having an almost shallow orthography. We have also identified a set of essential phonemes required to describe Alsatian phonology. In future work, we plan to increase the size of the pronunciation dictionary using a semi-automated approach relying on the G2P models described in Section 5.. The pronunciation dictionaries could be used in the future to create writing aids (spellchecking), or to assist dictionary lookup for spelling variants.

Figure 1 :

 1 Figure 1: Example of a grapheme-phoneme alignment by Phonetisaurus. The phonetic transcription uses X-SAMPA.

Figure 3 :

 3 Figure 3: Typical temporal patterns of the Alsatian plosives: top = fortis plosive, bottom = lenis plosive. The rectangles frame three intra-segmental phases : 1, the Voice Termination Time, 2, the silent stop gap, and 3, the burst and Voice Onset Time.

Table 1 :

 1 Summary of data resources

		DICTMULTI	1,594	7,277
		ELSASSICH	702	4,372
		OLCA67	1,458	10,825
		OLCA68	1,401	10,472
	German	MARY VOX	26,233 8,463	218,669 55,688
	French	LEX3	125,733	834,011

Table 2 :

 2 G2P results. The best results for each dataset have a grey background. The results of the closed tests are in italics.

		DICTMULTI	ELSASSICH	OLCA67	OLCA68
	Training dataset	WER	PER	WER	PER	WER	PER	WER	PER
	DICTMULTI	11.70%	4.49% 82.75% 29.83% 88.98% 35.49% 90.70% 38.02%
	ELSASSICH	88.76 % 37.59% 47.37% 19.04% 96.12% 45.32% 97.14% 48.59%
	OLCA67	71.59% 27.45% 85.67% 31.90% 23.87%	7.18% 66.52% 20.22%
	OLCA68	72.33% 29.14% 85.67% 33.19% 67.02% 20.84% 29.30%	9.52%

Training on the largest datasets (DICTMULTI and OLCA67) yielded the best results. The results obtained by training on OLCA67 and then applying the model to OLCA68 (and the other way round) could indicate that the use of the same spelling system plays a positive role. However, it should be mentioned that OLCA67 and OLCA68 also roughly correspond to the same vocabulary set, only for two different dialectal areas,

See e.g. the Trésor de la Langue Française French dictionary (http://atilf.atilf.fr/tlf.htm) which uses pseudo-phonetic input or the Picartext textual database for the Picard language (https://www.u-picardie.fr/ LESCLaP/PICARTEXT/Public/) which matches word forms based on phonetic correspondences.

Acknowledgements

This work was supported by the French "Agence Nationale de la Recherche" (ANR) (project no.: ANR-14-CE24-0003). We would like to thank Paul Adolf for kindly providing an electronic copy of his dictionary.


Roxana Agrigoroaie

Adriana Tapus

Physiological Parameters Variation Based on the Sensory Stimuli used by a Robot in a News Reading Task

Enabling robots to determine how physiological parameters vary in relation to the profile of an individual can lead to a better adaptation of the behavior of the robot to the needs of the individual it interacts with. This paper investigates how physiological parameters (i.e., blinking, galvanic skin response (GSR), facial temperature variation) vary in a news reading task, based on various types of stimuli (i.e., auditory and visual) that TIAGo robot uses to present the news. The results from a within participant study with 11 participants are reported. We also consider the impact of personality and the user sensory profile (based on Adult and Adolescent Sensory Profile (AASP)) for the physiological parameters variation. Our results show that blinking is the main physiological parameter that varies in the non-stressing task of news reading.

I. INTRODUCTION

An extensive research was conducted in HRI with the purpose of adapting the behavior of a robot to the individual it interacts with. Multiple aspects have been used for the adaptation. Most notable aspects are: the affective state of the individual [START_REF] Liu | Online affect detection and robot behavior adaptation for intervention of children with autism[END_REF], proxemics [START_REF] Walters | The influence of subjects' personality traits on personal spatial zones in a human-robot interaction experiment[END_REF], personality traits [START_REF] Tapus | Hands-off therapist robot behavior adaptation to user personality for post-stroke rehabilitation therapy[END_REF], and different body signals [START_REF] Mitsunaga | Robot behavior adaptation for hri based on policy gradient reinforcement learning[END_REF]. Recently, the authors of [START_REF] Agrigoroaie | Influence of robot's interaction style on performance in a stroop task[END_REF] have shown that the sensory profile of an individual can also be used.

The research also indicates that there are different physiological parameters (e.g., heart rate [START_REF] Appelhans | Heart rate variability as an index of regulated emotional responding[END_REF], respiration rate [START_REF] Chauvin | Contact-free respiration rate monitoring using a pan-tilt thermal camera for stationary bike telerehabilitation sessions[END_REF], [START_REF] Barbosa-Pereira | Remote monitoring of breathing dynamics using infrared thermography[END_REF], galvanic skin response (GSR) [START_REF] Nebylitsyn | Biological bases of individual behavior[END_REF], [START_REF] Nagai | Influence of sympathetic autonomic arousal on cortical arousal: implications for a therapeutic behavioural intervention in epilepsy[END_REF], facial temperature variation [START_REF] Ioannou | Thermal infrared imaging in psychophysiology: Potentialities and limits[END_REF], [START_REF] Ioannou | Proximity and gaze influences facial temperature: a thermal infrared imaging study[END_REF], facial expressions [START_REF] Ekman | Facial Action Coding System -Investigator's Guide[END_REF], blinking [START_REF] Bentivoglio | Analysis of blink rate patterns in normal subjects[END_REF], [START_REF] Tanaka | Blink activity and task difficulty[END_REF]) that can be used to measure how an individual reacts to different stimuli or in different situations.

A robot capable of measuring these parameters can better understand the internal state of the individual it interacts with. Thus, it can adapt its behavior to better suit the needs of that individual. For example, the robot could detect that the individual is stressed, and as a result it could lower its speech volume, interact at a greater distance and lower the amount of stimuli used throughout the interaction.

Our current research focuses on three physiological parameters: blinking, GSR, and facial temperature variation.

The authors of [START_REF] Ioannou | Thermal infrared imaging in psychophysiology: Potentialities and limits[END_REF] have shown that how the temperature varies across different regions on the face could indicate the emotional state of an individual. For example, a decrease of the temperature in the nose region could mean that the individual is stressed. Anxiety is characterized by an increase of the temperature in the periorbital and forehead regions.

The authors of [START_REF] Ioannou | Proximity and gaze influences facial temperature: a thermal infrared imaging study[END_REF] have performed a literature review to determine which are the facial regions that provide valuable information regarding the internal state of a person. They have found six regions of interest (i.e., nose, chin, cheeks, periorbital region, maxillary area, and the forehead).

The galvanic skin response (GSR) has been used by the authors of [START_REF] Nourbakhsh | Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks[END_REF] to measure the cognitive load of individuals in a reading task. It has also been used to determine the arousal level [START_REF] Nebylitsyn | Biological bases of individual behavior[END_REF], or the emotional state of an individual [START_REF] Lisetti | Using noninvasive wearable computers to recognize human emotions from physiological signals[END_REF].

The blinking rate of an individual changes based on the activity performed. It also relates to the difficulty level of the task performed [START_REF] Tanaka | Blink activity and task difficulty[END_REF]. A difficult task leads to an increase in blinking rate. The authors of [START_REF] Bentivoglio | Analysis of blink rate patterns in normal subjects[END_REF] found a mean blinking rate of 26 blinks/minute during a conversation, compared to 17 blinks/minute as the average resting blinking rate. Exposure to different visual or auditory stimuli can also affect the blinking rate [START_REF] Stern | The endogenous eyeblink[END_REF], [START_REF] Fukuda | Eye blinks: new indices for the detection of deception[END_REF]. A decrease in blinking rate has been found in cases of exposure to sensory stimuli.

The current study wants to investigate how the physiological parameters (i.e., blinking, GSR, facial temperature variation) vary depending on the personality of the participant and the type of sensory stimuli (i.e., visual, auditory) a robot uses in a news reading task. Another factor taken into account in this research is the interaction distance between the robot and the participants (i.e., personal and social distance [START_REF] Hall | The hidden dimension[END_REF]).

From the personality theories found in the literature (e.g., Big 5 model [START_REF] Goldberg | An alternative 'description of personality': the big-five factor structure[END_REF], Eysenck Personality Theory [START_REF] Eysenck | Dimensions of personality: 16, 5 or 3? -criteria for a taxonomic paradigm[END_REF], Gray's Reinforcement Sensitivity Theory [START_REF] Gray | The neuropsychology of anxiety: An enquiry into the functions of the septo-hippocampal system[END_REF]), two are considered in this research. These two have been selected (i.e., Eysenck Personality Theory, Reinforcement Sensitivity Theory) as both of them strongly relate physiological parameters to different personality traits.

In this paper we also use the model for the user's sensory profile introduced by Dunn [START_REF] Dunn | The impact of sensory processing abilities on the daily lives of young children and their families: A conceptual model[END_REF] for measuring how individuals respond to different stimuli. For example, individuals that have a high neurological threshold require high-intensity stimuli.

The paper is structured as follows: Section III describes how the data was recorded and analyzed. Section II presents the experimental design. In Section IV, the results are summarized. A short discussion is provided in Section V. The conclusions and a perspective on future works are part of Section VI.

II. EXPERIMENTAL DESIGN

A. Robotic platform and sensors

For this experiment the TIAGo robot [START_REF] Pages | Tiago: the modular robot that adapts to different research needs[END_REF] was used. It was developed by PAL Robotics1 in Spain and was customized for the requirements of the ENRICHME European H2020 research project. The robot (see Fig. 1) features a mobile base, a lifting torso, a touch-screen, and a head.

The speakers are mounted in the head of the robot. The head also features the RGB-D sensor. The Orbbec Astra RGB-D sensor provides RGB images of 640x640 at a frame rate of up to 30 frames/second. The Optris PI450 thermal camera, mounted on the head of the robot, has an optical resolution of 382x288 pixels at a frame rate of up to 80 frames/second.

The GSR sensor used was manufactured by Grove2 . The sensor was placed for all the participants on the ring and middle finger of the left hand.

B. Questionnaires

Each participant filled all of the following questionnaires: Eysenck Personality Questionnaire (EPQ) [START_REF] Eysenck | Manual of the Eysenck Personality Questionnaire (junior and adult)[END_REF] was developed to measure the four personality traits of the Eysenck personality model. The personality traits are: extraversion (E), neuroticism (N), psychotism (P), and lie/social desirability (L). The model proposes that each personality trait has a physiological basis. Extraversion is based on cortical arousal [START_REF] Eysenck | Psychophysiology and personality: Extraversion, neuroticism and psychoticism[END_REF], which can be measured by skin conductance, or by using electroencephalograms (EEG). Introverts show greater electrodermal activity than extraverts under non-stressing conditions [START_REF] Stelmack | The Psychophysiology of Extraversion and Neuroticism[END_REF]. Neuroticism is based on the activation of the limbic system [START_REF] Eysenck | Psychophysiology and personality: Extraversion, neuroticism and psychoticism[END_REF]. In Eysenck's view neuroticism is equivalent to the degree of emotionality [START_REF] Eysenck | The biological basis of personality[END_REF]. Psychotism is characterized by hostile, suspicious, impersonal, and agressive personality [START_REF] Eysenck | Psychoticism as a Dimension of Personality[END_REF]. It was shown that males tend to have much higher psychotism scores than females [START_REF] Eysenck | Psychophysiology and personality: Extraversion, neuroticism and psychoticism[END_REF]. As a result, testosterone levels could be used as an indicator of psychotism. Research confirms that the personality traits are determined by biological factors [START_REF] Eysenck | Dimensions of personality: 16, 5 or 3? -criteria for a taxonomic paradigm[END_REF]. Each of the personality traits are measured on a scale from 0 to 12. All results were categorized as either low (scores less than or equal to 6), or high (scores greater than 6).

The Reinforcement Sensitivity Theory Personality Questionnaire (RST-PQ) [START_REF] Corr | The reinforcement sensitivity theory of personality questionnaire: Development and validation[END_REF] is based on the theoretical analysis of the Reinforcement Sensitivity Theory (RST) proposed by Gray [START_REF] Gray | The neuropsychology of anxiety: An enquiry into the functions of the septo-hippocampal system[END_REF]. RST proposes that the personality of a person is based on three neurobehavioral systems. The Fight Flight Freeze System (FFFS) is responsible for the reactions towards aversive stimuli. The Behavior Activation System (BAS) is responsible for the reactions towards all rewarding ("appetitive") stimuli. The last system, the Behavior Inhibition System (BIS), is responsible for the reactions in the situations where there is conflict between approach (BAS) and avoidance (FFFS) situations. RST-PQ was developed to measure all three systems. For the BAS there are 4 subscales: Impulsivity (BAS-I), Reward Interest (BAS-RI), Reward Reactivity (BAS-RR), Goal-Driven Persistance (BAS-GDP). Each system is measured on a scale from 1 to 4. The results of the participants were categorized as either low (scores less than or equal to 2), or high (scores greater than 2).

Adult/Adolescent Sensory Profile Questionnaire (AASP) [START_REF] Brown | Adult/adolescent sensory profile: Self questionnaire[END_REF] was developed to measure the sensory profile of an individual. Dunn [START_REF] Dunn | The impact of sensory processing abilities on the daily lives of young children and their families: A conceptual model[END_REF] proposed a four-quadrant model to measure the responses to everyday sensory experiences. It was initially proposed for children, but research showed that it can be applied to adults as well [START_REF] Brown | The adult sensory profile: Measuring patterns of sensory processing[END_REF]. The model proposes that there is a relationship between the neurological threshold (low and high), and the behavioral response (accordance and counteract), which lead to fourquadrants: Low Registration (AASP-LR), Sensation Seeking (AASP-SS), Sensation Avoiding (AASP-SA), and Sensory Sensitivity (AASP-Sens). Each quadrant is characterized by six sensory processing categories (i.e., activity level, auditory, movement, taste/smell, touch, and visual). Each processing category is measured on a scale from 1 to 5. The results of the participants were categorized as either low (scores less than or equal to 2.5) or high (scores higher than 2.5).

C. Participants

Eleven participants (2 females and 9 males, with mean age of 26.81, SD=4.56) agreed to take part in this experiment. All participants have a technical background. The participants were required to answer on a scale from 1 ("Not at all") to 5 ("Very much") how much they knew about robotics. One participant reported no knowledge at all, 1 a little knowledge, 2 somewhat, 5 knew much, while 2 knew very much.

Table I summarizes the results of the participants to the questionnaires presented in Section II-B. A technical problem was encountered while recording the thermal data for two participants.

Therefore, the thermal data from the 9 participants was used for the analysis. The blinking and the GSR physiological parameters were analyzed for all 11 participants.

Based on the results from Table I the following questionnaire results were considered for further analysis: FFFS, and BAS-I (from RST), E, and N (from EPQ), and auditory AASP-LR, visual and auditory AASP-SS, auditory AASP-SA, and visual AASP-Sens from AASP.

D. Scenario

For the participants to take part in this experiment, they had to sign a consent form and to fill in the questionnaires presented in Section II-B. Each participant was escorted to the laboratory by the experimenter. Upon arriving in the experimental area, the experimenter explained what the experiment consisted of. Once the participants confirmed that they understood what they had to do, the experimenter left the experimental zone and the experiment started.

The task that the participants had to perform was a news reading task. For this purpose, the web-based News Application module of the ENRICHME H2020 research project was used. The module uses the RSS 2.0 feed of different newspapers to gather the latest news and display them to the user. During this experiment, the news from The Guardian newspaper, "World" category were presented. The news presented were the headlines for the experiment days. They were presented as they were received through the RSS feed. No limitation was imposed on the number of words.

For this experiment, 5 conditions have been designed. During each condition the robot interacted with the participants at three interaction distances: 70cm (D70), 1.2m (D120), and 2m (D200). These distances have been chosen as they correspond to the personal and social distances, as defined by Hall [START_REF] Hall | The hidden dimension[END_REF]. D120 and D200 correspond to the lower, and higher limit of the social distance.

One interaction is defined by a news being presented by the robot either by using auditory stimuli (reading the news out loud), visual stimuli (displaying the news on the tablet), or both auditory and visual stimuli (reading the news and displaying it on the tablet). Both the conditions and the interaction distances for each condition were selected in a random order.

When the robot presented the news by using visual stimuli, it either adapted the font of the text or not. In case of Fig. 2. Eyes feature points font adaptation, the font size was changed depending on the distance between the robot and the participant. The robot detected the face of the participant, and using depth information it computed the distance between its RGB-D sensor and the user. The unit of height of the viewport (vh) was used for the font size. The following font sizes were used: for distances less than 70 cm (D70) the font size was set to 4vh; for distances less than 1.2 m (D120) it was set to 5vh; while for distances greater than 1.2 m (D200) a font size of 6vh was used.

The robot was manually controlled by the experimenter. The five conditions are: In this section, the methods used to extract and analyze the data are described.

A. Blinking

To extract the blinks, the module first described in [START_REF] Agrigoroaie | Contactless physiological data analysis for users quality of life improving by using a humanoid social robot[END_REF] was used. The module can be used both in real-time and for offline processing. It was designed to be used as a Robot Operating System (ROS) [START_REF] Quigley | Ros: an open-source robot operating system[END_REF] module. It uses the Dlib toolkit [START_REF] King | Dlib-ml: A machine learning toolkit[END_REF] to detect the faces in the RGB data. Once the face was detected, the feature points of interest corresponding to the eye lids are localized (see Fig. 2). It requires an initialization period of 30 seconds in which the distance between the eye lids is stored in a buffer, after which the mean eye lid distance is computed for each eye. The initialization period is needed as there are multiple shapes and sizes of the eyes. Furthermore, the camera can be located at different distances from the user. The period can be set as a ROS parameter. For this experiment, the data was analyzed offline.

Once the mean eye lids distance is known, the blinks can be extracted from the RGB data. For each RGB frame the distance between the eye lids is computed. If the distance between the eye lids is less than 0.3 times the mean distance, Fig. 3. GSR and distance data for one participant then the eyes are considered to be closed. In case both eyes are closed for less than 300 ms, a blink has occurred.

For each detected blink a ROS custom message is published that contains the timestamp at which the blink occurred, and a constant value of 1. The following parameters were extracted and used for the statistical analysis: the total number of blinks, and the average blink rate (computed as the total number of blinks divided by the interaction duration and multiplied by 60 seconds).

B. GSR

For the GSR data, there are multiple parameters that can be used for analysis. Two parameters that are widely used are: the accumulative GSR [START_REF] Nourbakhsh | Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks[END_REF] and the number of peaks. As the output of the sensor is the resistance, first the signal had to be converted to conductance.

The signal was first divided into the three interaction distances. Next, the algorithm presented in [START_REF] Nourbakhsh | Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks[END_REF] was applied to extract the accumulative GSR (AccGSR) for each interaction distance of each condition. First, each participant's signal had to be normalized, by dividing the signal during one interaction by the mean value of all interactions of the participant (Eq (1) of [START_REF] Nourbakhsh | Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks[END_REF]). The AccGSR was extracted from the normalized signal (Eq (2) in [START_REF] Nourbakhsh | Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks[END_REF]). For the peaks, only the peaks that were at least 2% of the range of values were extracted.

A typical GSR signal during one of the conditions is shown in Fig. 3. The upper part of the figure presents the variation of the GSR signal and the detected peaks, while in the lower part of the figure is shown the interaction distance.

C. Thermal data

The temperature variation across different regions of interest (ROI) provides good insight into the current internal state of a user. Therefore, the thermal data was used to extract these temperature variations. No open source face detector has been found for thermal images. As a result, a thermal face detector has been trained using the Dlib object detector, which was implemented in Python. The object detector is based on the algorithm presented in [START_REF] Dalal | Histograms of oriented gradients for human detection[END_REF]. The detector was trained with 900 images from the Natural Visible and Infrared facial Expression Database (NVIE) [START_REF] Wang | A natural visible and infrared facial expression database for expression recognition and emotion inference[END_REF]. Images with and without glasses have been used for training.

To detect the ROIs, a feature point detector was trained for 11 feature points. The detector was trained using the Fig. 5. Temperatature variation over time Dlib shape predictor, which was implemented in Python. The shaper predictor, which uses regression trees, is based on the algorithm presented in [START_REF] Kazemi | One millisecond face alignment with an ensemble of regression trees[END_REF]. A total number of 493 images from the NVIE dataset and from our previous experiments have been used for training the detector. The 11 feature points of interest (i.e., the middle of the eyebrows, the inner and outer corners of the eyes, the corners and the tip of the nose, and the corners of the mouth) were selected as these are sufficient to define six ROIs on the face (see Fig. 4): the entire face, the forehead, the left and right periorbital regions, the nose, and the perinasal region.

As the interaction in this experiment took place at three different distances, the ROIs were defined based on the distance between the inner corners of the eyes (eyes dist) as follows (see Fig. 4 for the ROIs at the three interaction distances):

1) the entire face: The face region starts from eyes dist above the eyebrows and ends at eyes dist below the mouth. It has a width equal to the distance between the outer corners of the eyes, and the height equal to the distance between the eyebrows and the mouth plus two times eyes dist. 2) the forehead region: width equal to the distance between the middle of the eyebrows; and the height equal to the distance between the eyes and the nose. 3) the left, and right periorbital regions: both these regions were defined as square regions around the inner corners of the eyes with the side equal to 1/3 of eyes dist. 4) the nose: a square region around the tip of the nose, with the side equal to 1/3 of eyes dist 5) the perinasal region: was defined with a width equal to the distance between the corners of the mouth, and a height equal to the distance between one corner of the nose and the mouth at which a distance of 1/3 of eyes dist was added in order to include the nostrils too. Once the ROIs were defined, the mean temperature could be extracted together with the timestamp at which it occurred. To remove short variations that could lead to false measurements, the data was re-sampled with linear interpolation with a rate of 10 Hz. Next, a moving average filter with a window of 20 samples (5 seconds) was applied. Finally, a least-square regression was applied in order to fit a linear model on our data. Figure 5 shows an example of filtered data for the forehead region at a close interaction distance. The result of the linear regression was overlapped on the temperature data (the model was fitted with r 2 = 0.779 and p < 2.2e-16). In this case, the temperature increases with 0.00342 • C/s.

IV. RESULTS

As previously mentioned, the blinking and GSR data of all participants were used for the analysis, while for the thermal data, only the data from 9 participants was used. For all parameters the same type of analysis has been performed. First, it was checked to see if the dependent variables were normally distributed. For this purpose a Shapiro-Willks normality test was applied.

Only the AccGSR showed a normal distribution. Therefore, a mixed linear model analysis has been applied in order to determine if the condition, distance, interaction between condition and distance, stimulus type and font size are better predictors for the parameters than the mean value. The stimulus type could be auditory (C1), visual (C2, C3), or auditory/visual (C4, C5). The font size was either none (C1), fixed (C2, C4), or adapted (C3, C5). For the analysis based on the questionnaire results, a Kruskall-Wallis test was applied. Next, the significant results are going to be presented. A discussion based on there results is presented in Section V.

A. Blinking

First, the analysis was performed on the total number of blinks. All models, except the distance model, proved to be better predictors for the number of blinks than the mean model.

Next, we applied a general linear hypotheses post-hoc test on each of the models. For the condition model (F (1, 4) = 9.40, p < .0001) the following significant results were found: C2 -C1 (z = -4.55, p < 0.001), C3 -C1 (z = -5.43, p < 0.001), C5 -C1 (z = -4.7, p < 0.001), and C4 -C3 (z = 2.86, p = 0.034). A boxplot of the results is shown in Figure 6. During conditions C2, C3, and C5, the participants blinked significantly less than during conditions C1 and C4.

For the stimulus type model (F (1, 2) = 15.88, p < .0001) the significant differences were between auditory/visualauditory stimulus (z = -4.13, p < 0.001), and visualauditory (z = -5.66, p < e-04) (see Figure 7). When presented with only the auditory stimuli, the participants blinked significantly more than when either auditory/visual or just visual stimuli were used.

For the font size model (F (1, 2) = 16.39, p < .0001) significant differences were found between none -adapted (z = 5.77, p < 0.001), and none -fixed (z = 4.05, p < 0.001) (see Figure 8). When no visual stimuli were shown, the participants blinked significantly more than when either visual stimuli with a fixed or an adapted font size were presented. The differences between the fixed size font and the adapted font approach significance (z = 2.10, p = 0.088) with more blinks occurring for the fixed font size than for the adapted font size.

For the interaction between condition and distance (F (1, 14) = 3.05, p < .001) we found significant results for: C2D70 -C1D70 (z = -4.11, p < 0.01), C3D70 -C1D70 (z = -4.3, p < 0.01) C5D70 -C1D70 (z = -4.0, p < 0.01).

Next, we wanted to see if the number of blinks could be predicted based on the results of the questionnaires. The The same analysis applied on the blinking rate yielded the same significant factors.

B. GSR

The analysis was performed on the Accumulative GSR (AccGSR) and the number of peaks greater than 2% of the range of values.

Regarding the AccGSR, the condition, the stimulus and the interaction between condition and distance models proved to be better predictors than the mean model. A general linear hypotheses post-hoc test applied on the condition model (F (1, 4) = 15.64, p < .0001) yielded the following significant results (see Figure 9 for the boxplot of the results): C2 -C1 (z = -3.878, p < 0.001), C3 -C1 (z = -5.17, p < 1e-04), C4 -C2 (z = 4.324, p = 0.0001), C5 -C2 (z = 5.03, p < 1e-04), C4 -C3 (z = 5.626, p < 1e-04), C5 -C3 (z = 6.334, p < 1e-04). Significantly higher values for the AccGSR were found during conditions C1, C4, and C5 compared to conditions C2, and C3. When only visual stimuli were shown (conditions C2, and C3) the participants had lower levels of AccGSR than during the other conditions. Therefore, we can say that during these conditions their cognitive level was lower than when either auditory or a combination of auditory and visual stimuli were used [START_REF] Nourbakhsh | Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks[END_REF].

For the stimulus model (F (1, 2) = 30.18, p < .0001), the following significant results were found (see Figure 10): visual -auditory (z = -5.19, p < 1e-04), visual -auditory/visual (z = -7.48, p < 1e-04). As with the conditions, when only visual stimuli were used the participants had a lower cognitive level than when either only auditory or a combination of auditory and visual stimuli were used.

For the interaction between the condition and distance, some of the most significant results are: C5D120 -C2D120 (z = 4.681, p < 0.001), C2D200 -C1D200 (z = -4.461, p < 0.01), C4D200 -C2D200 (z = 4.229, p < 0.01). Next, the number of peaks were analyzed. None of the models were better predictors than the mean model. Therefore, we continued with the Kruskal-Wallis analysis based on the questionnaire traits.

The significant results are:

1) Personality (χ 2 = 13.52, p = 0.0002) 2) N (χ 2 = 3.90, p = 0.048) 3) FFFS (χ 2 = 4.4, p = 0.035) 4) auditory AASP-SS (χ 2 = 22.4, p = 2.3e-06) 5) visual AASP-SS (χ 2 = 11.04, p = 0.0008)

For the trait of FFFS, individuals with high scores had a significantly higher number of GSR peaks than individual with low scores. For personality, auditory and visual AASP-SS individuals with low scores showed a greater number of peaks than individuals with high scores. Figure 12 shows a boxplot of the number of GSR peaks based on personality.

C. Facial temperature variation

The rate of temperature change for the forehead, left, and right periorbital regions, nose, and face region was analyzed. None of the models proved to be better predictors for the temperature variation in either of the regions than the mean model. Thus, the Kruskal-Wallis test was performed on the questionnaire results. Significant results were found for only one trait, FFFS in the following regions:

1) forehead (χ 2 = 18.79, p = 1.45e-05) 2) nose (χ 2 = 9.63, p = 0.0019) 3) face (χ 2 = 10.88, p = 0.0009) In all three regions, individuals with low FFFS scores showed a significantly higher increase in temperature than individual with high scores. Figure 13 shows a boxplot of the rate of change of nose temperature based on FFFS score.

V. DISCUSSION

The number of participants who took part in this study is low (11 for the blinking and GSR analysis, and 9 for the temperature variation analysis). In order to really confirm the obtained results a new study should be performed with more participants. The participants had to perform a nonstressing task (i.e., reading or listening to news). Thus, the lack of significant results for GSR and temperature variation when using the RST and EPQ results as factors can be explained. Both RST and EPQ are based on the assumption that personality has a physiological basis. All their factors can be measured using physiological parameters and are more noticeable in stressing tasks.

In conditions C2, C3, C4, and C5 the participants had to read the news from the visual display (i.e., the touchscreen). The results that were found for the number of blinks depending on the condition are in accordance with the results in [START_REF] Bentivoglio | Analysis of blink rate patterns in normal subjects[END_REF], where the authors found a lower blinking rate for a reading task (4.5 blinks/minute), than for a resting situation (17 blinks/minute). Regarding the stimulus type, in both significant comparisons (auditory -auditory/visual, and auditory -visual), when presenting an auditory stimulus the participants blinked significantly more, than when a visual or a combination of visual and auditory stimuli were presented. This result can also be associated with the results found by the authors of [START_REF] Bentivoglio | Analysis of blink rate patterns in normal subjects[END_REF]. No significant differences were found in the number of blinks between the adaptive and the fixed font size. A tendency has been found for the adaptive font to lead to a lower number of blinks than the fixed size (p = 0.08). We believe that it was easier for the participants to read the font that was changed based on the interaction distance, than the fixed size one. The authors of [START_REF] Stern | The endogenous eyeblink[END_REF], [START_REF] Fukuda | Eye blinks: new indices for the detection of deception[END_REF] found that exposure to sensory stimuli lead to a decrease of the blinking rate. Based on how individuals respond to different sensory stimuli (see Section II-B), individuals with low scores inAASP-Sens should not be affected by sensory stimuli. As a result their total number of blinks should not decrease. Our results show that participants with low scores in visual AASP-Sens blinked significantly more than participants with high scores, which is in accordance with the results of [START_REF] Stern | The endogenous eyeblink[END_REF], [START_REF] Fukuda | Eye blinks: new indices for the detection of deception[END_REF].

For the GSR data, we found significant results for both parameters (i.e., AccGSR and peaks). A literature review showed that extraverted individuals are less aroused than introverted individuals. This means that in a non-stressing task introverts show greater electrodermal activity than extraverts [START_REF] Stelmack | The Psychophysiology of Extraversion and Neuroticism[END_REF]. Our results confirm this relationship.

The FFFS is responsible for the reactions towards aversive stimuli; therefore an individual with high score is more prepared to flee from a situation than an individual with low score. This means that high scores should be associated with a greater electrodermal activity and a lower increase in temperature [START_REF] Ioannou | Proximity and gaze influences facial temperature: a thermal infrared imaging study[END_REF]. Our results are in accordance with this theory. We found individuals with high scores having a significantly higher number of peaks and significantly lower increase in temperature than individuals with low scores.

To the best of our knowledge, no study has been performed in order to find the relationship between the sensory profile and GSR data, or the sensory profile and the facial temperature variation across different regions of interest. Further studies should be performed to investigate if the same results are obtained.

Even if the interaction between the robot and the participants was limited, we believe that in a similar study without a robot the results would have been different.

VI. CONCLUSION AND FUTURE WORK

In this paper, we presented a within participant study with 11 participants which was carried out to find out how different physiological parameters (i.e., blinking, GSR, facial temperature variation) varies in a non-stressing task (i.e., reading or listening to news) based on the type of stimuli presented and the personality of the individuals. For this purpose five conditions have been developed in which the TIAGo robot used different ways of presenting the information to the participants (i.e., only auditory stimuli, only visual stimuli, both auditory and visual stimuli). During each condition, the interaction between the robot and the participants took place at three distances (70 cm, 1.2 m, and 2 m). We used different psychological questionnaires (i.e., RST-PQ, AASP, EPQ) to determine if the results of these questionnaires are a good predictor of the physiological parameters. Significant results were found for how much participants blinked based on how the information was presented. Additionally, significant results were found for the relationship between GSR and extraversion. Some of our future work include the replication of the results found for the relationship between GSR and the sensory profile, and facial temperature variation and the sensory profile. We plan to perform a study that uses a stressing task in order to find if there is any correlation between the physiological parameters (i.e., blinking, GSR, temperature variation, facial expression). Furthermore, we want to find the connection between the psychological questionnaires and the variation of GSR and facial temperature.

Fig. 1 .

 1 Fig. 1. Laboratory setup with TIAGo Robot

Fig. 4 .

 4 Fig. 4. Example of ROIs at three interaction distances

Fig. 6 .Fig. 7 .

 67 Fig. 6. Number of blinks based on condition

Fig. 8 .

 8 Fig. 8. Number of blinks based on stimulus type

Fig. 9 .

 9 Fig. 9. Accumulative GSR based on condition

Fig. 10 .

 10 Fig. 10. Accumulative GSR based on stimulus type

Fig. 12 .

 12 Fig. 12. Number of GSR peaks based on personality

TABLE I PARTICIPANTS

 I DISTRIBUTION BASED ON QUESTIONNAIRES RESULTS

			RST-PQ
	Category FFFS BIS	BAS RI	BAS RR	BAS GDP	BAS I
	low	6	2	0	0		1	4
	high	5	9	11	11	10	7
				AASP
	Category	AASP-LR AASP-SS AASP-SA AASP-Sens
	low visual	10		4		8		6
	high visual	1		7		3		5
	low auditory	5		4		5		3
	high auditory	6		7		6		8
				EPQ
		Category E	P	N
		low		6	11	7
		high	5	0	4

  Sound and No Visual Interface • Condition 2. No Sound and Visual Interface with no font adaptation • Condition 3. No Sound and Visual Interface with font adaptation • Condition 4. Sound and Visual Interface with no font adaptation

• Condition 1. • Condition 5. Sound and Visual Interface with font adaptation III. DATA EXTRACTION AND ANALYSIS

www.pal-robotics.com

http://wiki.seeed.cc/Grove-GSR Sensor

*This work was funded and conducted in the context of the EU H2020 ENRICHME projet, Grant Agreement No:643691


Roxana Agrigoroaie

Adriana Tapus

The Outcome of a Week of Intensive Cognitive Stimulation in an Elderly Care Setup: A Pilot Test

In the context of a worldwide aging population, it is important to find solutions to help the elderly maintain their cognitive functions. This research was done in the context of the ENRICHME. We investigate the outcome of a 5 day intensive cognitive stimulation with an elderly individual. Each day was composed of two sessions (one at 11am, and one at 3pm). During each session the participant played three cognitive games (i.e., digit cancellation, integer matrix task, Stroop game), two of them having two difficulty levels. The mood of the participant was also recorded before and after each interaction session. Evidence was found that even after a few sessions, the performance of the participant increased for all games. The performance of each game and each difficulty level was analyzed based on the interaction time (11am or 3pm) and the interaction day. A detailed analysis of the performance is presented together with a discussion of these results.

I. INTRODUCTION

The worldwide population is growing older [START_REF] He | An aging world: 2015[END_REF]. A lot of research is dedicated to finding viable solutions for enabling the elderly to stay independent for longer in their own home environments, and to enhance their quality of life. One such solution for providing personalized care is represented by the socially assistive robots (SAR) [START_REF] Feil-Seifer | Defining socially assistive robotics[END_REF]. Some such robots have been developed in the past few years [START_REF] Coradeschi | Giraffplus: A system for monitoring activities and physiological parameters and promoting social interaction for elderly[END_REF], [START_REF] Papoutsakis | Developing visual competencies for socially assistive robots: the hobbit approach[END_REF], [START_REF] Stavropoulos | Automatic action recognition for assistive robots to support mci patients at home[END_REF]. They have been developed as it was shown that they can be used to stimulate cognitive functions [START_REF] Tanaka | Effect of a human-type communication robot on cognitive function in elderly women living alone[END_REF], [START_REF] Tapus | Music therapist robot for individuals with cognitive impairments[END_REF] and they can provide monitoring [START_REF] Coradeschi | Giraffplus: A system for monitoring activities and physiological parameters and promoting social interaction for elderly[END_REF]. The aim of the HOBBIT European project [START_REF] Papoutsakis | Developing visual competencies for socially assistive robots: the hobbit approach[END_REF] was to use a robot in order to visually observe and interpret the actions of the elderly so that appropriate assistive services can be provided. The RAMCIP European project [START_REF] Stavropoulos | Automatic action recognition for assistive robots to support mci patients at home[END_REF] focuses on how a robot can monitor the behaviour of an elderly individual with mild cognitive impairment in order to provide assistance in case of need. The Robot-Era 1 [START_REF] Di Nuovo | The multi-modal interface of robot-era multirobot services tailored for the elderly[END_REF] robotic system was designed as a mobile personal service robot with the aim of helping the elderly in their daily-life activities and to stimulate mental and physical exercise. The MoveCare 2 European research project developed a robotic system capable of providing with the help of artificial intelligence assistance and transparent monitoring for the elderly in their own homes.

Another research project for the elderly with mild cognitive impairment (MCI) is represented by the ENRICHME 3 EU Horizon2020 project. Its purpose is to develop a personal socially assistive robot that can enhance the everyday life of the elderly and to help them stay independent for longer. The robot adapts its behavior based on the user's profile, past experiences, cognitive disability level, emotional state, and other preferences. The profile of the user is determined based on multiple psychological questionnaires (e.g., Eysenck Personality Questionnaire, Morningness-Eveningness Questionnaire).

One important requirement of the ENRICHME system is to provide cognitive stimulation that is adapted for the elderly with MCI. For this purpose, multiple cognitive games have been developed specifically for this purpose. There are multiple platforms that provide cognitive games (e.g., Luminosity4 , M3W5 , Sociable 6 ), but none of them could be customized based on the elderly profile, preferences and robot's requirements. For each game that we developed, there is real time feedback, both written and spoken by the robot, and a customized feedback is provided at the end of the game. The variation of the performance is shown for the previous 6 days when exiting each game. This feature enables the elderly to see how they are progressing in each game.

As the ENRICHME system adapts based on the profile of the individual it interacts with, first it had to be investigated how the profile of an individual can be determined. For this purpose, multiple questionnaires have been used in our previous studies [START_REF] Agrigoroaie | Contactless physiological data analysis for users quality of life improving by using a humanoid social robot[END_REF], [START_REF]Influence of robot's interaction style on performance in a stroop task[END_REF] to determine which aspects of these questionnaires are of importance. We found evidence that the personality, neuroticism, empathy level, morningnesseveningness type, sensory profile, are all important traits that have to be taken into account.

The current study investigates how the cognitive performance of an elderly individual with mild cognitive impairment (MCI) varies after a week long intensive cognitive stimulation scenario. More specifically, the participant had to complete two cognitive sessions each day (one in the morning at around 11am, and one in the afternoon at around 3pm) for 5 days. The results of the first day are not considered for the analysis, as the influence of the novelty factor (the presence of the new experimenter and new difficulty levels for the games) could have an influence on the results. Of interest for this research is how the performance differs between morning and afternoon, and over time. The participant was already familiar with the robot, as he is Fig. 1: Experimental setup one of the users that tests the ENRICHME system for a longer period of time (i.e., 12 weeks). The participant is also familiarized with the cognitive games, but only with the easy levels and not with the difficult ones.

The paper is structured as follows. Section II presents the experimental design. We detail the scenario, present the robotic platform used, and describe the participant in more details. The cognitive games tested are presented in Section III. The results are summarized in Section IV. In Section V, a discussion of the results is provided, while the paper concludes with Section VI.

II. EXPERIMENTAL DESIGN A. Robotic platform and sensors

The robotic platform of the ENRICHME project is the TIAGo robot [START_REF] Pages | Tiago: the modular robot that adapts to different research needs[END_REF], developed by PAL Robotics7 in Spain. The robot was customized based on the requirements of the research project.

It features a mobile base, a lifting torso, a touch-screen mounted on the torso, a head that incorporates an Orbbec Asus RGB-D camera. An Optris PI450 thermal camera was mounted on the head of TIAGo (see Figure 1).

The data from the two cameras (i.e., RGB-D and thermal), together with the data from a Grove GSR 8 were recorded for physiological data analysis. The GSR sensor was positioned on the ring and middle fingers of the right hand. The participant wore a touch-screen glove on its left hand to ease the usage of the touch-screen.

B. Questionnaires

For this study, the participant had to complete the following questionnaires:

Eysenck Personality Questionnaire (EPQ) [START_REF] Eysenck | Manual of the Eysenck Personality Questionnaire (junior and adult)[END_REF] was developed to measure four personality traits: extraversion, psychotism, neuroticism, and lie. The Eysenck model proposes that each of these four traits has an underlying physiological basis. For this study of importance are the personality traits of extraversion (which is based on cortical arousal [START_REF] Eysenck | Psychophysiology and personality: Extraversion, neuroticism and psychoticism[END_REF]), and neuroticism (which Eysenck considered it to be equivalent to the degree of emotionality [START_REF]The biological basis of personality[END_REF]). Each trait is measured on a scale from 1 to 12 (e.g., individuals with scores lower than 6 are considered introverted, while individuals with scores higher than 6 are considered extraverted).

Morningness -Eveningness Questionnaire (MEQ) [START_REF] Horne | A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms[END_REF] is widely used in the literature to assess if a person is of a morning type or an evening type. It was shown [START_REF] Horne | A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms[END_REF] that evening individuals perform better cognitively in later hours of the day, than morning individuals. This questionnaire was used in order to understand whether being a morning type or an evening type can affect the cognitive performance.

Reinforcement Sensitivity Theory -Personality Questionnaire (RST-PQ) was developed by Corr [START_REF] Corr | The reinforcement sensitivity theory of personality questionnaire: Development and validation[END_REF] to determine the personality of an individual as defined by the Reinforcement Sensitivity Theory (RST) [START_REF] Gray | The neuropsychology of anxiety: An enquiry into the functions of the septo-hippocampal system[END_REF]. The model, which is a neuroscience model of personality, states that the personality of an individual is based on three neurobehavioral systems (Behavior Activation System, Behavior Inhibition System, Fight-Flight-Freeze System). As the Eysenck model, which was the starting point of RST, RST relates all personality traits to underlying physiological basis.

Positive And Negative Affect Schedule (PANAS) [START_REF] Watson | The development and validation of brief measures of positive and negative affect: the panas scales[END_REF] measures both positive and negative affect. Each of its 20 items are measured using a 5-point Likert scale. High values of possitive affect are associated with emotions like enthusiasm, or alertness, while low values for possitive affect is a characteristic of sadness. On the other hand, high values for negative affect are indicators of distress or unpleasurable engagement, while low values for negative affect represent the absence of any feeling [START_REF] Watson | Negative affectivity: the disposition to experience aversive emotional states[END_REF].

Custom developed post-questionnaires contains a series of questions to assess how the participant rated each interaction. Moreover, a 5 point Likert scale was used so that the participant could rate the following items: difficulty of each game, stressfullness of each game, length of each game, utility of robot/tablet feedback, etc.

C. Scenario

The experiment presented in this study took place in the house of an elderly participant, who is one of the testing users of the ENRICHME system. The house is part of a housing facility designed for the elderly from the United Kingdom. The interaction took place in the living room of the participant (see Figure 1).

Originally, the experiment was planned for two participants, both end user testers of the ENRICHME system. However, due to the degradation of the visual capabilities of one of the participants, he had to be removed from this study. As a result, the experiment was carried out with only one participant. From the original 3 days of interaction with each participant, it resulted a 5 day interaction with only one of them.

The interaction took place two times each day, a session in the morning (at around 11am), and the second session in the afternoon (at around 3pm). For the rest of this study, we consider the afternoon session as an evening session.

As the participant was not familiar with the experimenter, two days of visits were planned. In the first day, the experimental setup was put in place. While on the second day, the two interaction sessions took place. The data on this day was not considered for the analysis as we wanted to eliminate all influences of different novelty parameters (e.g., presence of new experimenter, the experiment, the difficult levels of the cognitive games).

Each of the 8 sessions considered for the analysis consisted of three main phases:

• Relaxation: we wanted the participant to feel relaxed before performing the cognitive activities. Furthermore, this relaxation part enabled us to determine the baseline for the GSR and to see variations in GSR between different times of day. • Cognitive games: the participant had to play 5 cognitive games. The order of the games was chosen by the participant, and each game had to be played only once.

A description of each game is presented in Section III. • News Reading: as a continuation of our previous study [START_REF] Agrigoroaie | News application adaptation based on user sensory profile[END_REF], we tested the news reading application with two interaction distances and different sensory stimuli (either only visual, or a combination of visual and auditory). Before the relaxation phase and after the news reading phase, the participant filled the PANAS questionnaire. After each interaction session, the participant had to also fill in the custom developed post-questionnaire.

D. Participant description

The participant is a 69 years old male from the United Kingdom. He is a retired self employed decorator. He used to live alone, but he moved to the housing facility after he fell and he broke his hip. Not very long ago, he injured his right shoulder, and even though he is right handed he had to use his left hand for interacting with the touchscreen of the robot. He has no previous knowledge about robotics, except the news that he sees on the television. He has been smoking for more than 40 years and he likes to drink coffee. He does not drink any alcohol.

The results from the questionnaires show that he is very extraverted (a score of 12 out of 12), and that he has a low neuroticism level (a score of 2 out of 12), he is an intermediate type when considering the morningness eveningness questionnaire, with a score of 56. The MEQ score can be between 16 and 86. Evening types have scores between 16 and 41; morning types have scores between 59 and 86.

His results from the RST-PQ show that his FFFS score is above average (3.3 out of 4), while his behavior inhibition score is very low (1.1 out of 4). Regarding the behavior activation system, for 2 of the 4 subscales (reward interest, and impulsivity) he scored below average (2.1 and 2.3 out of 4), while for the other two (reward reactivity and goal driven potential) he scored above average (2.6 and 3.1 out of 4).

III. COGNITIVE GAMES

The ENRICHME system offers a series of nine cognitive games (i.e., digit cancellation, letter cancellation, integer matrix task, decimal matrix task, memory game, hangman, puzzle, Stroop game, speed game), each with multiple difficulty levels. Out of these, three (see Figure 2) have been chosen for this study (i.e., digit cancellation, integer matrix task, and the Stroop game). These have been chosen as they exercise the following cognitive functions: attention, speed, mental arithmetic. Below is a description of each of the games.

A. Digit Cancellation

In this game, the user is presented with a list of a certain length which contains shuffled digits. In the given time (i.e., 50 seconds), the user has to find all occurrences of a randomly chosen digit in the given list. The color of a correctly chosen item is changed to green, while for a wrong answer it turns red. For this study two difficulty levels were developed. For the easy level the length of the list is equal to 20 (see Figure 3a), while for the difficult level the length of the list is equal to 40 (see Figure 3b). The game ends either when the user finds all the occurrences, or when the time is up. After the game a feedback is given with the number of mistakes made and the total game time.

B. Integer Matrix Task

In this game the user is shown a matrix with digits. The goal is to find the two digits whose sum equals 10. Feedback is given after each selection. Once the matrix is correctly solved, a new one is presented. If the user makes a mistake, the same matrix is shown until the correct answer is found. The final goal is to solve as many matrices as possible in a total time of 5 minutes. Two difficulty levels (see Figure 4) were developed: easy (Figure 4a) (matrix size of 2 x 2), and difficult (Figure 4b) (matrix size of 4 x 4).

C. Stroop

For this game, a variation of the original word-color Stroop test [START_REF] Stroop | Studies of interference in serial verbal reactions[END_REF] was developed. The user has to press on the button For each button press a feedback is given. There is no time limit, but the user has to find the right answer as quickly as possible. Before each stimulus was shown, a warning signal was given in the form of a fixation phase of 1 second. The purpose of this phase was to prepare the participant for the stimulus and to lead to a reduction of the reaction time [START_REF] Kahneman | Attention and effort[END_REF].

For this research the number of total trials of this game was set to 50. In this game there were two types of trials: congruent, and incongruent. In a congruent trial the color of the text is the same as the color depicted by the word written on the screen (e.g., the word Blue written in blue color). For the incongruent trial the color of the text was not the same as the color depicted by the word (e.g., the word Blue written in red color as shown in Figure 5).

IV. RESULTS

In this section, we present the results corresponding to the performance during each of the cognitive games and the results of the PANAS questionnaire. As the participant could choose the order of the games, we noticed that the participant preferred to start with the easy levels of each game, then continue with the Stroop game, and at the end to do all difficult levels of the games.

A. PANAS

The PANAS questionnaire was filled in before the relaxation part and after the news part. For each session we computed the difference between the mood after the interaction and before the interaction. We wanted to see if there are differences between the morning and evening sessions, and between the interaction days.

For the positive affect difference, an one-way ANOVA analysis shows that the participant felt better after the morning session than after the evening session (F(1,6)=0.94, p=0.368). On days day2 and day3 the participant felt better after the interaction than in the last two days (day4, day5) (F(3,4)=5.43, p=0.067). This result can be related to the participant's health. On the last two days he had some health problems and was not feeling good at all.

No differences were found for the negative affect. The negative affect recorded was at a constant value of 10 for 7 out of the entire 8 interaction sessions considered for the analysis. The only day in which the negative affect increased after the experiment, was on day3 in the evening session. As seen previously, an increased value for negative affect is associated with unpleasurable engagement. During that session, for the news reading application multiple technical difficulties were encountered, and the news phase had to be restarted multiple times.

B. Digit Cancellation

For the Digit Cancellation game three parameters were extracted and used for the analysis: total game time, average time to select an answer, and the total number of mistakes. The two factors used for analysis are the day when the sessions took place (day2, day3, day4, or day5), and the time of the session (morning, or evening). Figure 6a shows the variation of the total game time in each day for the easy level, while Figure 6b shows for the difficult level.

For the total game time, we found that in general our participant finished the easy level faster in the evening session than in the morning one (F(1, 6)=3.72, p=0.1) (Figure 7a). While for the difficult level the opposite was found (F(1, 6)=1.73, p=0.23) (Figure 7b). This is also due to the fact that the participant did not finish the difficult level 4 times out of the total 8 times taken into consideration for the analysis. No important differences were found for the total game time between the days.

For the average time to select an answer, we found that for the easy level in the morning hours the participant took longer to make a selection than in the afternoon hours (F(1, 6)=0.96, p=0.36). For the difficult level, the average time to make a selection was similar in the morning and evening sessions.

For the analysis based on the days, we found that for the difficult level, the participant took longer to make a selection in the first two days than in the last two days (F(3,4)=3.32, p=0.13) (Figure 8). No important results were found for the easy level.

During the easy level the participant made no mistakes. For the difficult level, the participant made the most mistakes in the evening hours (F(1,6)=2.359, p=0.175). Moreover, in the first two days more mistakes were made than in the last two days (F(1,6)=2.359, p=0.175).

Overall, even if no significant results were found, a tendency can clearly be seen. More interaction sessions and more participants could help find the significant results.

The results of the post questionnaire show that the participant did not find the easy level neither difficult, nor stressing. While for the difficult level, the participant reported that he found the game difficult, but not stressing.

C. Integer Matrix Task

As the participant really tried not to make any mistakes, only one mistake was made in all 8 interaction sessions. Therefore, only the number of correctly solved matrices and the time to find the first of the two digits were considered for the analysis. Figure 9a shows the number of correctly solved matrices for each day for the easy level, while Figure 6b shows for the difficult level.

For the total number of correctly solved matrices no differences were found for the easy level between the morning and evening sessions. For the difficult level, we found that the participant solved more matrices in the evening than in the morning (F(1,6)=3.54, p=0.1) (see Figure 10).

We found that in the first day the participant performed worse in the easy level than in the other days (F(3,4)=4.615, p=0.086), especially compared to day3 (p=0.22) and day4 (p=0.17). No important differences were observed for the difficult level.

When considering the time taken to find the first answer the following results were found. For the difficult level we found that the participant found more quickly the answers in the evening session than in the morning session For the analysis based on the day of interaction, for the easy level, it took longer to find the first element in day2 than in any other day (F(3,4)=5.4, p=0.06). For the difficult level, no differences were found between the days.

The participant did not find the easy level difficult, nor stressing, while the difficult level was considered difficult but not stressing.

D. Stroop game

For this game we only considered for the analysis the average reaction time and the total game time. Only three mistakes were found throughout the 8 sessions.

No differences were found between the morning and the afternoon sessions for the game time and neither for the average reaction time.

On day2 it took longer, on average, to find the answer than on the other days (F(3,4)=9.69, p=0.02), especially when compared to day3 (p=0.056), and day5 (p=0.052). For this study we only had one participant, therefore, the results that we obtained will have to be confirmed with more participants, including also females. This preliminary data shows that there is a tendency for this participant to perform differently in the morning and in the afternoon hours. Furthermore, we found evidence that there is an improvement over time.

The results from the Digit Cancellation show that the participant finished the game faster in the evening than in the morning for the easy level. No mistakes were made for the easy level, but some mistakes were made for the difficult one. Furthermore, for the difficult level, the participant did not finish the game 4 times out of the total 8 sessions. The number of mistakes decreased from the first two days to the last two days. We believe that with more sessions the participant would improve its performance even more.

Regarding the Integer Matrix Task, for the difficult level the participant solved more matrices in the evening than in the morning, and it took a shorter time to find the answer in the evening than in the morning. For the easy level, the participant showed an improvement after the first day, while for the difficult level, more sessions are needed to see a clear improvement.

A clear improvement over time was found for the Stroop game. After only one day the participant performed significantly better in the following days.

Our extraverted participant was more efficient at around 3pm than at around 11am, a result which is in accordance with the theory proposed by Colquhoun [START_REF] Colquhoun | Temperament, inspection efficiency, and time of day[END_REF]. For the morningness-eveningness level, we found that only for the Digit Cancellation difficult level the participant performed better in the morning than in the evening. Further studies have to be devised in order to investigate if the same results are found for other elderly with MCI.

One of the questions in the custom developed postquestionnaire asked the participant if he would have preferred to do the same tasks on a tablet. After each interaction he said that he preferred to interact with the robot.

VI. CONCLUSION

In conclusion, this study represents a first attempt in how the cognitive performance of an individual with mild cognitive impairment varies through an interaction of 5 days. Moreover, it was investigated if there are differences between morning and evening sessions. The results obtained give us a starting point for future studies, in which, more participants could take part in a long-term cognitive stimulation study. Some of our future work include the analysis of the physiological parameters, to investigate if they are in accordance with the performance results. Furthermore, the data from the News Reading application will be analyzed.

Fig. 2 Fig. 3 :

 23 Fig. 2: Cognitive games

Fig. 4 :Fig. 5 :

 45 Fig. 4: Matrix Task: (a) Easy level; (b) Difficult level

Fig. 6 :Fig. 7 :

 67 Fig. 6: Digit Cancellation total game time over time: (a) Easy level; (b) Difficult level

Fig. 8 :

 8 Fig. 8: Digit cancellation average time to make a selection based on the day

Fig. 9 :Fig. 10 :

 910 Fig. 9: Integer Matrix correct answers: (a) Easy level; (b) Difficult level

Fig. 11 :

 11 Fig. 11: Stroop Game average reaction time based on interaction day

www.luminosity.com

https://m3w-project.eu/

http://www.cognitivetraining.eu/

www.pal-robotics.com

www.seeed.cc/Grove-GSR Sensor

*This work was funded and done in the context of the EU H2020 ENRICHME projet, Grant Agreement No:643691