Wearable Technology for Real-Time Monitoring of Stress and Behavior in Autistic Individuals in the USA

Abstract

This research investigates the development and deployment of wearable technology to monitor stress and behavioral patterns in real time of patients with Autism Spectrum Disorder (ASD). Physiological and behavioral data are collected by wearable devices like smartwatches and biosensors and then analyzed with machine learning algorithms for improving personalized care and crisis management. These technologies address the needs of autistic individuals by facilitating autonomy, reducing caregiver burden, and improving health outcomes. In this mixed methods study, the effectiveness of these devices is evaluated based on quantitative metrics such as accuracy and usability and qualitative feedback from users and caregivers. The results suggest that stress detection and behavior tracking can be improved significantly and that scalable solutions in ASD care are possible.