Resting state electroencephalogram in autism spectrum disorder identification based on neuro-physiological interface of affect (NPIA) modelling

Children with autism spectrum disorder (ASD) is likely to have repetitive and restricted repertoire in its behaviors, activities and interests. Early detection and intervention of ASD can help these children to lead an almost normal life. Thus it is important to ensure that early detection of such ASD preschoolers can be carried out. The brain connectivity of ASD can be achieved better by capturing and analyzing through the EEG and machine learning. In this paper we presented both the time domain approach, which were used by most researchers to identify ASD and also the neuro-physiological interface of affect (NPIA) at resting state. There seems to be consistency in results based on the NPIA at resting state for eyes opened and eyes closed while using time domain approach shows otherwise. Therefore, both models can be used to have a better accuracy in diagnosing an ASD. Future works also can have the NPIA model approaches on the other learning disabilities

A machine learning-based monitoring system for attention and stress detection for children with Autism Spectrum Disorders

© 2021 ACM, Inc. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1145/3484377.3484381The majority of children with Autism Spectrum Disorders (ASD) have faced difficulties in sensory processing, which affect their ability of effective attention and stress management. Children with ASD also have unique patterns of sensory processing when responding to the stimuli in the environment. In this study, a real-time monitoring system has been designed and developed for attention and stress detection. Comprehensive sensory information, including environmental, physiological, and sensory profile data can be collected by the system using sensors, smart devices, and a standard sensory profiling questionnaire. Data acquisition with 35 ASD children using the system prototype was successfully conducted. With the acquired data set, different machine learning models were trained to predict attentional and stress level. Among all the investigated models, Gradient Boosting Decision Tree and Random Forest obtained the best prediction accuracies of 86.67% and 99.05% on attention and stress detection respectively. The two models were then implemented into the system for automatic detection. Future work could be focusing on exploring more supportive features to improve the prediction accuracy for attention detection. Such an easily-accessed monitoring system tailored for children with ASD could be widely-used in daily life to assist ASD users with their attention and stress management

Chronic-Pain Protective Behavior Detection with Deep Learning

In chronic pain rehabilitation, physiotherapists adapt physical activity to patients' performance based on their expression of protective behavior, gradually exposing them to feared but harmless and essential everyday activities. As rehabilitation moves outside the clinic, technology should automatically detect such behavior to provide similar support. Previous works have shown the feasibility of automatic protective behavior detection (PBD) within a specific activity. In this paper, we investigate the use of deep learning for PBD across activity types, using wearable motion capture and surface electromyography data collected from healthy participants and people with chronic pain. We approach the problem by continuously detecting protective behavior within an activity rather than estimating its overall presence. The best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross validation. When protective behavior is modelled per activity type, performance is mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This performance reaches excellent level of agreement with the average experts' rating performance suggesting potential for personalized chronic pain management at home. We analyze various parameters characterizing our approach to understand how the results could generalize to other PBD datasets and different levels of ground truth granularity.Comment: 24 pages, 12 figures, 7 tables. Accepted by ACM Transactions on Computing for Healthcar

Monitorage des mesures physiologiques et des comportements répétitifs associés au stress chez les enfants ayant un trouble du spectre de l’autisme

Le trouble du spectre de l’autisme se caractérise par la présence de difficultés au plan de la communication sociale et par la présence de comportements répétitifs et d’intérêts restreints (American Psychiatric Association, 2013). Les enfants ayant un TSA présentent plusieurs difficultés concomitantes qui les rendent plus susceptibles de vivre des niveaux de stress élevés, comme des déficits dans la sphère de la communication, de la socialisation et des fonctions exécutives, ainsi que la présence de particularités sensorielles (Groden et al., 1994, 2005). Malgré que ces enfants soient plus à risque de vivre du stress, plusieurs enjeux méthodologiques rendent difficile sa mesure et plus particulièrement chez ceux qui sont non verbaux. Pour ces raisons, le recours aux mesures physiologiques pour évaluer le stress auprès de cette clientèle est d’une grande pertinence. Par contre, les sensibilités sensorielles de ces enfants pourraient les rendre plus susceptibles de ne pas tolérer ces mesures. Le premier article de cette thèse vise donc à évaluer l’efficacité de l’intervention comportementale renforcement différentiel d’autres comportements (differential reinforcement of other behavior; DRO) pour augmenter la tolérance au port d’une ceinture cardiaque chez deux enfants non verbaux ayant un TSA. Les résultats obtenus démontrent que cette intervention a été efficace pour amener ces enfants à augmenter leur tolérance au port du dispositif cardiaque. Un autre aspect qui a reçu beaucoup d’attention dans les dernières années est l’implication du stress dans l’explication des comportements répétitifs et stéréotypés chez les personnes ayant un TSA. Les résultats des études antérieures sont contradictoires (de Vaan et al., 2018; Gabriels et al., 2013; Hutt et al., 1975; Lydon et al., 2015; Yang et al., 2015) et ont principalement utilisé des mesures indirectes des comportements répétitifs. Pour cette raison, le deuxième article de cet ouvrage vise à évaluer le lien entre le cortisol salivaire, le rythme cardiaque et des mesures d’observations directes de la stéréotypie chez quatre enfants minimalement verbaux ayant un TSA. Les résultats montrent que le cortisol et le rythme cardiaque sont significativement liés à la stéréotypie globale et motrice, sans que ces liens soient observés avec la stéréotypie vocale. Finalement, mesurer directement les comportements stéréotypés demande beaucoup de ressources, ce qui pourrait expliquer la prépondérance des mesures indirectes dans les études. Comme pour l’évaluation du stress, il importe de réfléchir aux méthodes alternatives abordables et accessibles qui pourraient améliorer la mesure de ces comportements. Le troisième article de cette thèse a évalué l’efficacité d’un algorithme d’intelligence artificielle (IA) dans la reconnaissance de la stéréotypie vocale chez des enfants ayant un TSA. Les résultats démontrent que la performance de l’algorithme est supérieure à la reconnaissance due au hasard. Bien que des recherches futures soient nécessaires pour augmenter l’efficacité de cette méthode, l’IA représente une technologie novatrice ayant le potentiel d’améliorer significativement les méthodes actuellement utilisées pour mesurer la stéréotypie. En conclusion, le présent ouvrage explore différentes avenues novatrices pour mieux comprendre et de monitorer la stéréotypie chez les enfants ayant un TSA.Autism spectrum disorder (ASD) is characterized by the presence of difficulties in social communication and the presence of repetitive behaviors and restricted interests (American Psychiatric Association, 2013). Children with ASD have several concurrent difficulties, such as deficits in communication, socialization, and executive function, as well as the presence of sensory peculiarities that make them more likely to experience high levels of stress (Groden et al., 2005). Although these children are at increased risk for stress, a number of methodological issues make it difficult to measure, particularly in non-verbal children. For these reasons, the use of physiological measures to assess stress among this group is highly relevant. On the other hand, the sensory sensitivities of these children could potentially make them more likely to be intolerant to these measures. Therefore, the first study in this thesis aims to evaluate the effectiveness of differential reinforcement of other behavior (DRO) to increase compliance with wearing a heart rate monitor in two non-verbal children with ASD. The results obtained portray that this intervention was effective in getting these children to increase their compliance to wearing a cardiac device. Another aspect that has received much attention in recent years is the involvement of stress in explaining repetitive behaviors in individuals with ASD. However, the results of previous studies have been producing contradictory results (de Vaan et al., 2018; Gabriels et al., 2013; Hutt et al., 1975; Lydon et al., 2015; Yang et al., 2015), and have mainly been using indirect measures of stereotypy. For this reason, the second study in this thesis aims to evaluate the relationship between salivary cortisol, heart rate, and direct observational measures of stereotypy in four minimally verbal children with ASD. The results show that cortisol and heart rate are significantly related to global and motor stereotypy, but not to vocal stereotypy. Finally, measuring stereotypy requires a lot of resources, which could explain the preponderance of indirect measuring in studies on stress. As with the measurement of stress, it is important to consider affordable and alternative methods that could improve the measurement of these behaviors, and therefore the third study evaluated the effectiveness of an artificial intelligence (AI) algorithm in the recognition of vocal stereotypy in children with ASD. The results show that the performance of the algorithm is superior to recognition due to chance. Although future research is needed to increase the effectiveness of this method, AI represents an innovative technology with the potential to significantly improve the methods currently used to measure vocal stereotypy. In conclusion, this thesis explores different innovative methods to better understand and monitor stereotypy in children with ASD

Multimodal Data Analysis of Dyadic Interactions for an Automated Feedback System Supporting Parent Implementation of Pivotal Response Treatment

abstract: Parents fulfill a pivotal role in early childhood development of social and communication skills. In children with autism, the development of these skills can be delayed. Applied behavioral analysis (ABA) techniques have been created to aid in skill acquisition. Among these, pivotal response treatment (PRT) has been empirically shown to foster improvements. Research into PRT implementation has also shown that parents can be trained to be effective interventionists for their children. The current difficulty in PRT training is how to disseminate training to parents who need it, and how to support and motivate practitioners after training. Evaluation of the parents’ fidelity to implementation is often undertaken using video probes that depict the dyadic interaction occurring between the parent and the child during PRT sessions. These videos are time consuming for clinicians to process, and often result in only minimal feedback for the parents. Current trends in technology could be utilized to alleviate the manual cost of extracting data from the videos, affording greater opportunities for providing clinician created feedback as well as automated assessments. The naturalistic context of the video probes along with the dependence on ubiquitous recording devices creates a difficult scenario for classification tasks. The domain of the PRT video probes can be expected to have high levels of both aleatory and epistemic uncertainty. Addressing these challenges requires examination of the multimodal data along with implementation and evaluation of classification algorithms. This is explored through the use of a new dataset of PRT videos. The relationship between the parent and the clinician is important. The clinician can provide support and help build self-efficacy in addition to providing knowledge and modeling of treatment procedures. Facilitating this relationship along with automated feedback not only provides the opportunity to present expert feedback to the parent, but also allows the clinician to aid in personalizing the classification models. By utilizing a human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the classification models by providing additional labeled samples. This will allow the system to improve classification and provides a person-centered approach to extracting multimodal data from PRT video probes.Dissertation/ThesisDoctoral Dissertation Computer Science 201