A REVIEW ON EDGE AI FOR LOW-LATENCY HEALTH MONITORING IN WEARABLE IOT DEVICES: CHALLENGES AND FUTURE DIRECTIONS - Naeem Akbar Channar; Muhammad Yaqoob Koondhar; Saima Shaikh; Farah Naveen Issani; Noor Nabi Dahri; Zulfikar Ahmed Maher; Ali Ghulam (Spectrum of Engineering Sciences, 2025)
The increasingly popular concept of Edge Artificial Intelligence (Edge AI) has the potential to revolutionize health monitoring by allowing wearable devices to locally process health data on-device. This kind of approach obviates the need to transmit data to the cloud, and provides for reduced response latency, power consumption, and privacy concerns. In this review, we investigate how Edge AI is used for the real-time health monitoring with wearable IoT devices. An initial set of 42 papers was selected using title and abstract keywords matching, 21 High-quality articles were isolated for full review after using these inclusion & exclusion criteria available since 2020 to the beginning of 2025 and will concentrate on the state of the art in Edge AI for wearable health monitoring applications. Discuss an array of challenges including the power constraints and low sampling resolution of the devices used, and present future directions for advancing applications of Edge AI systems. The goal of this article is to offer a straightforward