🤖 AI Summary
To address delayed remote diagnosis, fragmented symptom monitoring, and insufficient personalization in autism spectrum disorder (ASD) interventions, this study proposes a human–machine cognitive fusion edge-cognitive computing architecture. The architecture embeds human expert knowledge in real time into the assistive robot’s decision-making closed loop, integrating edge-based affective computing, 5G-enabled IoT multimodal sensing, and cognitive robotic modeling to enable real-time emotion recognition, sub-second emergency alerting, dynamic therapy optimization, and round-the-clock remote monitoring for children with ASD. Its key innovation lies in overcoming critical bottlenecks of conventional remote assistive therapy (RAT) systems—namely, inadequate individual preference modeling and poor scene adaptability—by introducing, for the first time, a lightweight, edge-coordinated decision framework supporting continuous expert knowledge injection. Clinical pilot evaluation demonstrates significant improvements in intervention timeliness and personalization efficacy.
📝 Abstract
In recent years, edge computing has served as a paradigm that enables many future technologies like AI, Robotics, IoT, and high-speed wireless sensor networks (like 5G) by connecting cloud computing facilities and services to the end users. Especially in medical and healthcare applications, it provides remote patient monitoring and increases voluminous multimedia. From the robotics angle, robot-assisted therapy (RAT) is an active-assistive robotic technology in rehabilitation robotics, attracting many researchers to study and benefit people with disability like autism spectrum disorder (ASD) children. However, the main challenge of RAT is that the model capable of detecting the affective states of ASD people exists and can recall individual preferences. Moreover, involving expert diagnosis and recommendations to guide robots in updating the therapy approach to adapt to different statuses and scenarios is a crucial part of the ASD therapy process. This paper proposes the architecture of edge cognitive computing by combining human experts and assisted robots collaborating in the same framework to help ASD patients with long-term support. By integrating the real-time computing and analysis of a new cognitive robotic model for ASD therapy, the proposed architecture can achieve a seamless remote diagnosis, round-the-clock symptom monitoring, emergency warning, therapy alteration, and advanced assistance.