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Sensor-based Severity Estimation of Autism Spectrum Disorder to Aid Robot-enhanced Therapy
Abstract
In our research, we are attempting to predict Autism Spectrum Disorder (ASD) and the associated Autism Diagnostic Observation Schedule (ADOS) scores using data from the body skeleton, head movement, and eye gaze. To the best of our knowledge, no such prior work has been completed. ASD is a neurological and developmental disorder that affects how people interact with others, communicate, learn, and behave. Scores from the Autism Diagnostic Observation Schedule (ADOS) are regarded as a standard tool for making an early diagnosis of autism. Successful treatment of ASD requires proper diagnosis and methodical therapy plans. Conventional treatments of ASD usually involve diverse intervention techniques designed by professional therapists. Unfortunately, highly trained therapists are not always readily available. Accessible therapists may sometimes lack experience and observational skills, making it difficult to assist ASD children effectively. So the question is, can we find an alternative to Standard Human Therapy (SHT) in the form of Robot Assisted or Robot Enhanced Therapy (RET)? Our work contributes by proposing a RET system based on 3D body joints and gaze information. We investigated the publicly available "DREAM" dataset, having bio-marker information on 61 children diagnosed with ASD. We propose a feature vector that is based on traditional directly connected body joints as well as some unconventional non-attached body joints with close association. We attempted to predict the severity of the disorder based on our predicted ASD levels and ados scores. The goal of our developed system is to effectively assist RET in ASD diagnosis and therapy.
... These approaches also present the possibility of collecting data and building a social robot that may help children with ASD and reduce the demand for traditional therapeutic resources such as humans. In recent years, Robot-Enhanced Therapy (RET) [8]- [10], has significant attention on developing a social robot that facilitates interactive experiences for children with ASD. Several methods exist for collecting data and recognizing human activity, including Electromyography [11] and Accelerometers [12], which utilize sensors. ...
... Researchers in the study conducted by [14] explored the effectiveness of adopting both gazetracking data and skeleton-based data for recognizing human activity. Gaze-tracking and skeleton-based data represent notable techniques that afford us a valuable opportunity to collect data from the behaviors and interactions of children with ASD [10]. Microsoft Kinect with integrating external eye-tracking devices can track the movements of multiple people within its field of view and record the direction of a person's gaze. ...
Children with autism spectrum disorder (ASD) require long-term care, support, and empathy from experienced therapists. However, there is a shortage num-ber of highly experienced therapists available to provide consistent and high-quality care for all ASD children and teach them effectively. The Robotic-based assessment and treatment offer promising advantages for ASD children. However, many robotic and virtual therapies rely on pre-programmed that may not consider individual needs and cultural differences. This study aims to develop and test the hypothesis that robots can effectively assist in conducting therapy sessions for children with ASD. A Support Vector Machine (SVM) classifier has been used to predict out-comes from sckeleton and gaze basaed features of data generated during both robot-child and therapist-child in-teractions. A comprehensive evaluation across five distinct scenarios, incorporating various combinations of Robot-Enhanced Therapy (RET), Standard Human Treatment (SHT), and their concatenation have been conducted. The article results for both skeleton-based features and gaze-based data reveal a significant performance advantage over existing state-of-the-art methodologies.
We present a dataset of behavioral data recorded from 61 children diagnosed with Autism Spectrum Disorder (ASD). The data was collected during a large-scale evaluation of Robot Enhanced Therapy (RET). The dataset covers over 3000 therapy sessions and more than 300 hours of therapy. Half of the children interacted with the social robot NAO supervised by a therapist. The other half, constituting a control group, interacted directly with a therapist. Both groups followed the Applied Behavior Analysis (ABA) protocol. Each session was recorded with three RGB cameras and two RGBD (Kinect) cameras, providing detailed information of children’s behavior during therapy. This public release of the dataset comprises body motion, head position and orientation, and eye gaze variables, all specified as 3D data in a joint frame of reference. In addition, metadata including participant age, gender, and autism diagnosis (ADOS) variables are included. We release this data with the hope of supporting further data-driven studies towards improved therapy methods as well as a better understanding of ASD in general.
Background
Autism spectrum disorders (ASD) are a group of complex neurodevelopmental disorders. The prevalence of ASD in many South Asian countries is still unknown. The aim of this study was to systematically review available epidemiological studies of ASD in this region to identify gaps in our current knowledge.
Methods
We searched, collected and evaluated articles published between January 1962 and July 2016 which reported the prevalence of ASD in eight South Asian countries. The search was conducted in line with the PRISMA guidelines.
Results
We identified six articles from Bangladesh, India, and Sri Lanka which met our predefined inclusion criteria. The reported prevalence of ASD in South Asia ranged from 0.09% in India to 1.07% in Sri Lanka that indicates up to one in 93 children have ASD in this region. Alarmingly high prevalence (3%) was reported in Dhaka city. Study sample sizes ranged from 374 in Sri Lanka to 18,480 in India. The age range varied between 1 and 30 years. No studies were found which reported the prevalence of ASD in Pakistan, Nepal, Bhutan, Maldives and Afghanistan. This review identifies methodological differences in case definition, screening instruments and diagnostic criteria among reported three countries which make it very difficult to compare the studies.
Conclusions
Our study is an attempt at understanding the scale of the problem and scarcity of information regarding ASD in the South Asia. This study will contribute to the evidence base needed to design further research and make policy decisions on addressing this issue in this region. Knowing the prevalence of ASD in South Asia is vital to ensure the effective allocation of resources and services.
We propose a 3D gaze-tracking method that combines accurate 3D eye- and facial-gaze vectors estimated from a Kinect v2 high-definition face model. Using accurate 3D facial and ocular feature positions, gaze positions can be calculated more accurately than with previous methods. Considering the image resolution of the face and eye regions, two gaze vectors are combined as a weighted sum, allocating more weight to facial-gaze vectors. Hence, the facial orientation mainly determines the gaze position, and eye-gaze vectors then perform minor manipulations. The 3D facial-gaze vector is first defined, and the 3D rotational center of the eyeball is then estimated; together, these define the 3D eye-gaze vector. Finally, the intersection point between the 3D gaze vector and the physical display plane is calculated as the gaze position. Experimental results show that the average gaze estimation root-mean-square error was approximately 23 pixels from the desired position at a resolution of .
Outstanding skills, including special isolated skills (SIS) and perceptual peaks (PP) are frequent features of autism. However, their reported prevalence varies between studies and their co-occurrence is unknown. We determined the prevalence of SIS in a large group of 254 autistic individuals and searched for PP in 46 of these autistic individuals and 46 intelligence and age-matched typically developing controls. The prevalence of SIS among autistic individuals was 62.5 % and that of PP was 58 % (13 % in controls). The prevalence of SIS increased with intelligence and age. The existence of an SIS in a particular modality was not associated with the presence of a PP in the same modality. This suggests that talents involve an experience-dependent component in addition to genetically defined alterations of perceptual encoding.
Electronic supplementary material
The online version of this article (doi:10.1007/s10803-014-2296-2) contains supplementary material, which is available to authorized users.
This article presents the mechatronic design of the autonomous humanoid robot called NAO that is built by the French company Aldebaran-Robotics. With its height of 0.57 m and its weight about 4.5 kg, this innovative robot is lightweight and compact. It distinguishes itself from existing humanoids thanks to its pelvis kinematics design, its proprietary actuation system based on brush DC motors, its electronic, computer and distributed software architectures. This robot has been designed to be affordable without sacrificing quality and performance. It is an open and easy-to-handle platform. The comprehensive and functional design is one of the reasons that helped select NAO to replace the AIBO quadrupeds in the 2008 RoboCup standard league.
An indispensable part of life is social interaction. Without any doubt, it can help to overcome Autism Spectrum Disorder (ASD). ASD is defined as an abnormality in social communication, which is not a disease. So, to face this disorder, involvement in social communication is required. It can be possible through therapy sessions. In this modern era, not only the human but also a robot can play the role of the interaction partner during these sessions. To examine the behavior of the child with ASD, data is required, which is not possible to get all the time. From this thought, the DREAM dataset paves the way to evaluate the Robot Enhanced Therapy and records the data of the 61 children with ASD. The dataset provides the skeleton-based, gaze-based features, which were recorded by the RGB-D cameras. Besides, characteristics of the therapy and age, gender, ID number of a child are also provided. In this paper, we have proposed a method to classify the tasks (Imitation, Joint Attention, and Turn-Taking) accomplished by those children provided in the dataset. Further analysis is also executed by us to assess if a robot can be a substitute for humans to conduct the therapy session. Skeleton joint positions and gaze-based vectors are utilized to draw out the angles, distances between joints, and the coordinate direction angles, Direction Gaze Zone (DGZ), respectively. From the skeleton-based approach, statistical features (such as mean, median, standard deviation, minimum, and maximum), and from the gaze-based approach, mean frequency, the number of peaks of a signal of the frequency domain are obtained. Therefore, the entire analysis is evaluated from two perspectives. The ensemble methods such as Random Forest classifier, XGBoost classifier, Extra Trees classifier are deployed to obtain the predicted result from the test data. The results are satisfactory considering the challenge and complexity of the ASD domain. We have explored how robots can be an alternative to humans for the improvement of social communication among children with ASD.
https://ieeexplore.ieee.org/abstract/document/9638874
It is evident that recently reported robot-assisted therapy systems for assessment of children with autism spectrum disorder (ASD) lack autonomous interaction abilities and require significant human resources. This paper proposes a sensing system that automatically extracts and fuses sensory features such as body motion features, facial expressions, and gaze features, further assessing the children behaviours by mapping them to therapist-specified behavioural classes. Experimental results show that the developed system has a capability of interpreting characteristic data of children with ASD, thus has the potential to increase the autonomy of robots under the supervision of a therapist and enhance the quality of the digital description of children with ASD. The research outcomes pave the way to a feasible machine-assisted system for their behaviour assessment.
Automated Detection Approaches to
- S Rahman
- S F Ahmed
- O Shahid
Rahman, S., Ahmed, S.F., Shahid, O. et al. Automated
Detection Approaches to Autism Spectrum Disorder
Based on Human Activity Analysis: A Review. Cogn
Comput
(2021).