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Robotic Social Therapy on Children with Autism: Preliminary Evaluation Through Multi Parametric Analysis

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Talk at SocialCom2012 (Amstrdam).

Autism Spectrum Disorder (ASD) is a neural development disorder characterized by specific patterns of behavioral and social difficulties. Beyond these core symptoms, additional problems such as absence of gender differences identification, interactional distortions of environmental and family responses are often present. Taking into account these emotional and behavioral problems researchers and clinicians are hardly working to design innovative therapeutic approaches aimed to improve social capabilities of subjects with ASD.
Thanks to the technological and scientific progresses of the last years, nowadays it is possible to create human-like robots with social and emotional capabilities. Furthermore it is also possible to analyze physiological signals inferring subjects' psycho-physiological state which can be compared with a behavioral analysis in order to obtain a deeper understanding of subjects reactions to treatments.
In this work a preliminary evaluation of an innovative social robot-based treatment for subjects with ASD is described.
The treatment consists in a complex stimulation and acquisition platform composed of a social robot, a multi-parametric acquisition system and a therapeutic protocol.
During the preliminary tests of the treatment the subject's physiological signals and behavioral parameters have been recorded and used together with the therapists' annotations to infer the subjects' induced reactions. Physiological signals were analyzed and statistically evaluated demonstrating the possibility to correctly discern the two groups (ASD and normally developing subjects) with a classification percentage higher than $92\%$. Statistical analysis also highlighted the treatment capability to induce different affective states in subjects with ASDs more than in control subjects, demonstrating that the treatment is well designed and tuned on ASDs deficits and behavioral lacks.

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Robotic Social Therapy on Children with Autism: Preliminary Evaluation Through Multi Parametric Analysis

  1. 1. Robotic Social Therapy on Children with AutismPreliminary Evaluation Through Multi Parametric AnalysisDaniele Mazzei, University of PisaSocialCom2012First International Workshop on Wide Spectrum Social Signal ProcessingAmsterdam 3th September 2012
  2. 2. ASDs and Robotics• One of the main difficulties in subjects with autism spectrum disorders (ASDs) is their inability to understand and analyze the emotional state of their interlocutor.• Recent research shows that ASDs perceive robots not as machines, but as their artificial partnersR. Picard, “Future affective technology for autism and emotion communication,” Philosophical Transactions ofthe Royal Society B: Biological Sciences, vol. 364, pp. 3575–3584, dic 2009
  3. 3. The FACET HypothesisIDIA project has been founded by Italian Ministry of HealthThe FACE of Autism, Mazzei et. All, ROMAN2010, Viareggio Italy, Sept. 2010
  4. 4. What we need?
  5. 5. The FACE AndroidHappiness Disgust Anger FearSadness Surprise
  6. 6. The FACE Android
  7. 7. The FACET protocol Phase 3: Exposition and Phase 5 interpretation of Shared FACE’s attention expressions Phase 1: Baseline 0 min Subject – Robot/Therapist interaction  20 minrecording (5 min) Phase 2: Phase 4: Familiarization Exposition and Phase 6: with the FACET interpretation of Free room and the the therapist’s Play android expressions5 ASDs (all males) with QI higher than 805 normally developing N.Dev (11 males and 4 females)Age: 6-12 years
  8. 8. Multi input analysis method
  9. 9. Physiological signal processing: ECG • ECG filtered and HRV extracted • HRV signal was divided in 6 phases according to the therapists’ annotations • Features extracted with Kubios[Kubios] M. P. Tarvainen, et all, “Kubios hrv-a software foradvanced heart rate variability analysis,” in 4th EuropeanConference of the International Federation for Medical andBiological Engineering, IFMBE Proceedings 2009, vol.22, 2009, pp. 1022–1025.
  10. 10. Physiological signal processing: EDR• Signal trend removed (only phasic component)• Low pass filters at 2 and 0.2 Hz obtaining 2 signals• Derivative signals extraction• Division in phasesExtractedFeatures:• Area Under the Curve• Mean Amplitude• Number of Peaks
  11. 11. Data analysis• Each feature was normalized by subtracting the correspondent baseline phase value• Three analysis steps: 1. Assessing the homogeneity of the two groups (ASDs, Control) 2. Identifying statistical significant differences between protocol phases 3. Classifying populations and phases automatically
  12. 12. Group homogeneity assessment Kruskal-Wallis TestIn general: EDR features p-value > 0.7 and HRV features p-value > 0.05
  13. 13. Phases statistical differences analysis Mann-Whitney test HRV features do not discriminate phase 2 and 3 p-value > 0.05 EDR features statistically discriminate phase 2 and 3! p-value < 0.05
  14. 14. Classification• Features dataset reduced using PCA• Selected the first 15 principal components that describe 90% of the variance• Pattern recognition algorithm based on the K-Nearest Neighborhood non-parametric classifier• Supervised classifier
  15. 15. Population classification Normal developing and ASD subjects population classification Classification Norm. Dev ASD percentage > 92% Norm. Dev 92.50 ± 12.49 6.67 ± 7.46 ASD 7.50 ± 12.49 93.33 ± 7.46 Physiological signals acquired during the interaction with FACE allow to classify ASDs and N.Dev! Other subjects could be classified in blind using this trained classifier
  16. 16. Phases classification • Only phase 2 and 3 could be classified ASD subjects phase 2 and 3 classificationIn ASD population phases Phase 2 Phase 3recognized with percentage > 85% Phase 2 89.7436 ± 8.58 14.5299 ± 7.35 FACET protocol phase 2 and 3 Phase 3 10.2564 ± 8.58 85.4701 ± 7.35 are able to induce different psycho-physiological reactions in ASDs but not in N.Dev! Normal developing subjects phase 2 and 3 classificationIn N.Dev population phasesrecognized with percentage > 65% Phase 2 65.50 ± 21.90 38.5000 ± 24.55 Phase 3 33.50 ± 21.90 61.5000 ± 24.55
  17. 17. Behavioral analysis• All ASDs followed FACE Shared Attention Success 100% 100% during shared attention 80% task 60% 60% 40%• 55% of ASDs established 20% spontaneous conversation 0% with FACE ASD N.Dev Spontaneus conversation with FACE 60% FACE is able to trigger 50% 55% ASDs attention 40% ASDs are more attracted 30% 30% by FACE than N.Dev 20% 10% 0% ASD N.Dev
  18. 18. Expressions labeling • Happiness, Anger and Sadness well labeled • Difficulties in Fear, Disgust and Surprise recognition FACE and therapist expressions induce similar results Facial expressions labeling difficulties can be related to the subjects’ age in accordance with literature*S. Widen and J. Russell, “Children acquire emotion categories gradually”, CognitiveDevelopment, vol. 23, no. 2, pp. 291–312, 2008
  19. 19. Conclusions• FACET is well accepted by ASDs• Able to induce different reactions in ASD and N.Dev subjects• Protocol able to induce in ASDs different reactions among phases 2 and 3• Well designed for triggering attention in ASDs
  20. 20. Conclusions• Thanks to its predictable and stereotyped nature FACE perfectly fits ASDs behavioral attitude• EDR may be a good candidate for ASD treatment protocols and therapies evaluation © Fondazione ARPA Pictures by Enzo Cei
  21. 21. Future Works• On going experiments on control and ASD subjects• More tests of the FACET and HIPOP hardware/software infrastructure• Use of the FACET platform to perform generic studies on human-robot empathic links
  22. 22. Thanks For Your Attention Questions? CEEDs · The Collective Experience of Empathic Data Systems Project number: 258749 Call identifier: FP7-ICT-2009-5
  23. 23. IDIA and FACET conclusions• FACET protocol is able to evoke different reactions in normally developing and ASDs subjects• Facial expressions Labeling difficulties in accordance with littirature1• Thanks to its predictable and stereotyped nature FACE perfectly fits ASDs behavioral attitude © Fondazione ARPA Pictures by Enzo Cei[1] S. Widen and J. Russell, “Children acquire emotion categories gradu-ally,” Cognitive Development, vol.23, no. 2, pp. 291–312, 2008
  24. 24. Multi input analysis method • 2 Therapists in the control room annotate Therapist• Multi parameter comparison allows to infer conversation, answers and separately subject’s behavioral psycho- annotations complex subject behaviors and reactions relevant actions physiological • The therapist in the FACET room quick annotates signals relevant subjects actions • Videos are used to annotate subjects answers to Self reports facial expressions labeling tasks • Videos are used to annotate subjects reactions to • The three therapist use FACET videos to identify shared attention task and conversations with phases time references. FACE and psychologist Complex Social Behavior Analysis
  25. 25. Physiological signal analysis: ECG• ECG: – Moving average – QRS identification through Pan-Tompkins algorithm and R peak extraction – RR intervals (tachogram) calculation• EDR: – Moving average for trend extraction (tonic component) – De-trend (Only phasic component is considered) – Low passed at 2 and 0.2 Hz (two filtered signals are obtained)
  26. 26. FACET platform

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