Task Offloading for Smart Glasses in Healthcare: Enhancing Detection of Elevated Body Temperature
This addresses the challenge of battery drain and processing limitations for smart glasses in healthcare applications, such as COVID-19 detection, but is incremental as it focuses on optimizing existing offloading methods.
The paper tackled the problem of limited computational capabilities in smart glasses for healthcare monitoring by analyzing task-offloading scenarios to improve performance metrics like task completion time and energy consumption, demonstrating its practicality in a use case for detecting elevated body temperature in indoor settings.
Wearable devices like smart glasses have gained popularity across various applications. However, their limited computational capabilities pose challenges for tasks that require extensive processing, such as image and video processing, leading to drained device batteries. To address this, offloading such tasks to nearby powerful remote devices, such as mobile devices or remote servers, has emerged as a promising solution. This paper focuses on analyzing task-offloading scenarios for a healthcare monitoring application performed on smart wearable glasses, aiming to identify the optimal conditions for offloading. The study evaluates performance metrics including task completion time, computing capabilities, and energy consumption under realistic conditions. A specific use case is explored within an indoor area like an airport, where security agents wearing smart glasses to detect elevated body temperature in individuals, potentially indicating COVID-19. The findings highlight the potential benefits of task offloading for wearable devices in healthcare settings, demonstrating its practicality and relevance.