IoT ‐Enabled Bi‐ LSTM Framework for Monitoring Irregular Physical Activities in Patients With ASD

Autism Spectrum Disorder (ASD) is characterized by significant behavioral and social impairments, including irregular physical activities that may pose risks of self‐harm. Effective real‐time monitoring of these behaviors is crucial for enabling timely interventions and enhancing patient care. This study presents an innovative IoT‐inspired edge computing framework that employs a deep learning‐based Bidirectional Long Short‐Term Memory (Bi‐LSTM) network to detect abnormal physical activities in individuals with ASD. The framework facilitates the processing of wearable sensor data on GPU‐enabled edge devices, ensuring low‐latency recognition of movement patterns. Evaluation of the proposed system was conducted using the iFogSim simulator, yielding a remarkable accuracy of 95.83% in identifying irregular activities while significantly reducing computational overhead compared to conventional cloud‐based methods. The findings underscore the potential of edge‐based analytics to deliver efficient real‐time monitoring, thereby assisting caregivers in managing ASD‐related movement irregularities. Future research will aim to incorporate multimodal sensing techniques to further enhance behavioral analysis.