Behavioral Data Analysis of Robot-Assisted Autism Spectrum Disorder (ASD) Interventions Based on Lattice Computing Techniques

Abstract

Recent years have witnessed the proliferation of social robots in various domains including special education. However, specialized tools to assess their effect on human behavior, as well as to holistically design social robot applications, are often missing. In response, this work presents novel tools for analysis of human behavior data regarding robot-assisted special education. The objectives include, first, an understanding of human behavior in response to an array of robot actions and, second, an improved intervention design based on suitable mathematical instruments. To achieve these objectives, Lattice Computing (LC) models in conjunction with machine learning techniques have been employed to construct a representation of a child’s behavioral state. Using data collected during real-world robot-assisted interventions with children diagnosed with Autism Spectrum Disorder (ASD) and the aforementioned behavioral state representation, time series of behavioral states were constructed. The paper then investigates the causal relationship between specific robot actions and the observed child behavioral states in order to determine how the different interaction modalities of the social robot affected the child’s behavior.

Effectiveness of a Robot-Assisted Psychological Intervention for Children with Autism Spectrum Disorder

“…The robot was also used to collect real-time information regarding the session based on machine vision algorithms to recognise actions and gestures and speech recognition algorithms to recognise answers (Lytridis et al, 2022 ). In each session the robot recorded logs (i.e.…”

Facial Emotion Recognition-based Engagement De-tection in Autism Spectrum Disorder

“…Features extraction is an important intermediate step in many subsequent facial processing processes that range from biometric recognition to understanding mental states. Facial landmarking is used to localize and represent salient regions of the face such as the eyes, eyebrows, nose, mouth, and jawline [34]. www.ijacsa.thesai.org Facial landmarks have been successfully applied to face alignment, head pose estimation, face swapping, blink detection, and much more.…”

Fully robotic social environment for teaching and practicing affective interaction: Case of teaching emotion recognition skills to children with autism spectrum disorder, a pilot study

“…Thus, in these robots, additional materials, such as real or cartoon-like images were used to teach facial expressions by giving explanations, instructions, or feedback to children. In addition, there were studies that focused on detecting the emotional and behavioral states of children with ASD for better affective interaction with ASD children ( Rudovic et al., 2018 ; Kim et al., 2021 ; Silva et al., 2021 ; Lytridis et al., 2022 ; Valagkouti et al., 2022 ). In these studies, different features, such as facial expressions, gestures, and eye gaze duration, were used to measure the engagement and affective state of ASD children.…”