An Experimental Trial: Multi-Robot Therapy for Categorization of Autism Level Using Hidden Markov Model

Abstract

Several robot-mediated therapies have been implemented for diagnosis and improvement of communication skills in children with Autism Spectrum Disorder. The proposed research uses an existing model i.e., Multi-robot-mediated Intervention System (MRIS) in combination with Hidden Markov Model (HMM) to develop an infrastructure for categorizing the severity of autism in children. The observable states are joint attention type (low, delayed, and immediate) and imitation type (partial, moderate, and full) whereas the non-observable states are (level of autism i.e., (minimal, and mild). The research has been conducted on 12 subjects in which 8 children were in the training session with 72 experiments over 9 weeks, and the remaining 4 subjects were in the prediction test with 25 experiments for 6 weeks. The predicted category was compared with the actual category of autism assessed by the therapist using Childhood Autism Rating Scale. The accuracy of the proposed model is 76%. Further, a statistically significantly moderate Kappa measure of agreement between Childhood Autism Rating Scale and our proposed model has been performed in which n = 25, k = 0.52, and p = 0.009. This research contributes towards the usefulness of Hidden Markov Model integrated with joint attention and imitation modules for categorizing the level of autism using multi-robot therapies.

Social Robots: A Promising Tool to Support People with Autism. A Systematic Review of Recent Research and Critical Analysis from the Clinical Perspective

“…Thus, the knowledge and experience of clinical therapists in ASD are crucial for its proper use. We believe it is relevant to report that one study indicated an association between severity and stimulus preference (Ali et al, 2021). Stimulus preference has also been linked to perceptive sensitivity, as demonstrated by two studies involving children, showing that perceptive sensitivity influences the interaction with the robot and the outcomes of the robot-mediated intervention (Chevalier et al, 2022).…”

“…The authors suggested that this method could effectively screen for high-severity ASD with a high level of confidence. In the last study aiming to assess ASD severity, 12 individuals with mild or minimal severity, as assessed by the Childhood Autism Rating Scale (CARS) (Schopler et al, 1980), interacted with two NAO robots (Ali et al, 2021). The participants were recorded by a Kinect, with the operator in a separate room.…”

“…However, the most significant characteristic to consider is the symptomatologic severity of these individuals. Autism severity is associated with a preference for the type of stimuli used in a reinforcement paradigm, with children of lower severity tending to prefer visual stimuli and children with milder severity favouring physical ones (Ali et al, 2021). It also appears that different levels of severity are linked to varying levels of engagement during interactions with the robot, as children with more severe symptoms exhibit fewer instances of eye gaze and facial expressions (Ahmad et al, 2017).…”

Author verification of Nahj Al-Balagha

“…Firstly, the algorithm establishes for every single node a strong connection to its nearest neighbor (stylistically similar text) and two weaker connections to the first and second runner-up. As a result, the final network will contain a number of links, some of them being close (most similar), some others revealing weaker connections between samples resulting in a consensus tree [19,20,1]. "…”