2026-03-16T00:19:14Z https://ipsj.ixsq.nii.ac.jp/oai

oai:ipsj.ixsq.nii.ac.jp:00239430 2025-01-19T08:17:00Z 1164:8228:11483:11716

Leveraging Large Language Models to Enhance Understanding of Accelerometer Data on Physical Fatigue Detection Question Answering Leveraging Large Language Models to Enhance Understanding of Accelerometer Data on Physical Fatigue Detection Question Answering Elsen, Ronando Sozo, Inoue Elsen, Ronando Sozo, Inoue ケアにおける認識と分析 In this paper, we enhance the ability of Large Language Models (LLMs) to interpret accelerometer data for detecting physical fatigue. While sensors are increasingly used to monitor physical conditions, analyzing their data remains challenging. We focus on improving LLMs' question-answering capabilities, specifically for data generated from physical activities. To address the complexities of sensor data, we introduce and compare two input formats―list-based and graph-based representations. Using models like gpt-3.5-turbo and gpt-4o-mini in both zero-shot and few-shot scenarios, we find that graph-based formats significantly boost LLM performance, achieving a top F1-score of 67.50%. This research highlights the importance of refining data formats and enhancing model capabilities to improve LLMs' effectiveness in health monitoring applications. In this paper, we enhance the ability of Large Language Models (LLMs) to interpret accelerometer data for detecting physical fatigue. While sensors are increasingly used to monitor physical conditions, analyzing their data remains challenging. We focus on improving LLMs' question-answering capabilities, specifically for data generated from physical activities. To address the complexities of sensor data, we introduce and compare two input formats―list-based and graph-based representations. Using models like gpt-3.5-turbo and gpt-4o-mini in both zero-shot and few-shot scenarios, we find that graph-based formats significantly boost LLM performance, achieving a top F1-score of 67.50%. This research highlights the importance of refining data formats and enhancing model capabilities to improve LLMs' effectiveness in health monitoring applications. 情報処理学会 2024-09-19 eng technical report https://ipsj.ixsq.nii.ac.jp/records/239430 2189-4450 AA1271737X 研究報告高齢社会デザイン（ASD） 2024-ASD-30 19 1 8 https://ipsj.ixsq.nii.ac.jp/record/239430/files/IPSJ-ASD24030019.pdf application/pdf 2.3 MB 2026-09-19