Task Offloading for Smart Glasses in Healthcare: Enhancing Detection of Elevated Body Temperature

TL;DR

This paper analyzes task offloading for smart glasses in healthcare, focusing on detecting elevated body temperature to improve performance and energy efficiency in real-world scenarios.

Contribution

It evaluates optimal offloading conditions for healthcare monitoring on smart glasses, considering performance metrics and practical indoor use cases.

Findings

Offloading reduces energy consumption and improves task completion time.

Optimal offloading conditions depend on device capabilities and environment.

Demonstrates practicality of offloading in healthcare scenarios like temperature detection.

Abstract

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.