Improving Fatigue Detection with Feature Engineering on Physical Activity Accelerometer Data Using Large Language Models

Abstract

In this paper, we improve the classification performance of fatigue detection on physical activity using Large Language Models (LLMs). Fatigue is a critical reminder of a person’s health condition. Currently, studies on fatigue detection solely focus on sensor data to measure body condition. However, changing sensor data for more meaningful inference and improving fatigue detection performance needed to be developed. In this study, we implement LLMs for fatigue detection in physical activity. We use LLMs in the preprocessing steps to generate meaningful features that can improve classification performance. For evaluation, we study the prompt design of LLMs to investigate their effect on improving machine learning performance, and compare evaluation metrics between traditional machine learning and LLMs-based machine learning. Using LLMs, our proposed model achieves better performance for fatigue detection, especially in physical activity, with performance improvement of 2% minimum and 4.5% maximum.