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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
Daniele Mazzei, University of Pisa
SocialCom2012First International Workshop on Wide Spectrum Social Signal Processing Amsterdam 3th September 2012
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 partners
R. Picard, “Future affective technology for autism and emotion communication,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 364, pp. 3575–3584, dic 2009
The FACET Hypothesis
A THERAPIST-GUIDED SOCIAL INTERACTION BETWEEN A ROBOT ABLE TO EXPRESS EMOTIONS AND
ASDS CAN HELP THEM TO IMPROVE THEIR SOCIAL SKILLS.
IDIA project has been founded by Italian Ministry of Health
The FACE of Autism, Mazzei et. All, ROMAN2010, Viareggio Italy, Sept. 2010
This hypothesis strongly depends on the capability of the treatment to convey affective stimuli involving
the subjects
What we need?
Interaction protocol
FACE RobotHIPOP
acquisition platform
FACET (FACE THERAPY)
The FACE Android
Happiness
Anger
Sadness
Disgust
Fear
Surprise
The FACE Android
The FACET protocol
Subject – Robot/Therapist interaction
Phase 2:Familiarization with the FACET room and the
android
Phase 3:Exposition and
interpretation of FACE’s
expressions
Phase 4:Exposition and
interpretation of the therapist’s
expressions
Phase 5Shared
attention
20 min0 min
Phase 1:Baseline
recording (5 min)
Phase 6:Free Play
5 ASDs (all males) with QI higher than 805 normally developing N.Dev (11 males and 4 females)Age: 6-12 years
Multi input analysis method
• 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 for advanced heart rate variability analysis,” in 4th European Conference of the International Federation for Medical and Biological Engineering, IFMBE Proceedings 2009, vol. 22, 2009, pp. 1022–1025.
Physiological signal processing: ECG
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 phases
Extracted Features:• Area Under the
Curve • Mean Amplitude• Number of
Peaks
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
Group homogeneity assessmentKruskal-Wallis Test
In general: EDR features p-value > 0.7 and HRV features p-value > 0.05
Phases statistical differences analysis Mann-Whitney test
EDR features statistically discriminate
phase 2 and 3!p-value < 0.05
HRV features do not
discriminate phase 2 and 3p-value > 0.05
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
Population classification
Normal developing and ASD subjects population classification
Norm. Dev ASD
Norm. Dev 92.50 ± 12.49 6.67 ± 7.46
ASD 7.50 ± 12.49 93.33 ± 7.46
Classification percentage > 92%
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
Phases classification• Only phase 2 and 3 could be classified
ASD subjects phase 2 and 3 classificationPhase 2 Phase 3
Phase 2 89.7436 ± 8.58 14.5299 ± 7.35
Phase 3 10.2564 ± 8.58 85.4701 ± 7.35
Normal developing subjects phase 2 and 3 classification
Phase 2 65.50 ± 21.90 38.5000 ± 24.55
Phase 3 33.50 ± 21.90 61.5000 ± 24.55
In ASD population phases recognized with percentage > 85%
In N.Dev population phases recognized with percentage > 65%
FACET protocol phase 2 and 3 are able to induce different psycho-physiological reactions in ASDs but not in N.Dev!
Behavioral analysis• All ASDs followed FACE
during shared attention task
• 55% of ASDs established spontaneous conversation with FACE ASD N.Dev
0%10%20%30%40%50%60%70%80%90%
100%100%
60%
Shared Attention Success
ASD N.Dev0%
10%
20%
30%
40%
50%
60%
55%
30%
Spontaneus conversation with FACE
FACE is able to trigger ASDs attention
ASDs are more attracted by FACE than N.Dev
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”, Cognitive Development, vol. 23, no. 2, pp. 291–312, 2008
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
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
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
Thanks For Your Attention
Questions?
mazzei@di.unipi.it
www.faceteam.it
CEEDs · The Collective Experience of Empathic Data SystemsProject number: 258749 Call identifier: FP7-ICT-2009-5
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
[1] S. Widen and J. Russell, “Children acquire emotion categories gradu-ally,” Cognitive Development, vol. 23, no. 2, pp. 291–312, 2008
© Fondazione ARPA Pictures by Enzo Cei
Multi input analysis method
Complex Social Behavior Analysis
Self reports
psycho-physiological
signals
Therapist behavioral
annotations
• 2 Therapists in the control room annotate separately subject’s conversation, answers and relevant actions
• The therapist in the FACET room quick annotates relevant subjects actions
• The three therapist use FACET videos to identify phases time references.
• Videos are used to annotate subjects answers to facial expressions labeling tasks
• Videos are used to annotate subjects reactions to shared attention task and conversations with FACE and psychologist
• Multi parameter comparison allows to infer complex subject behaviors and reactions
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)
FACET platform
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