Deep Learning for Frailty Classification Using Raw Inertial Sensor Gait Data

Arslan Amjad, Agnieszka Szczęsna, Monika Błaszczyszyn

2025

Abstract

The Frailty is a significant health issue in older adults that increases the risk of disability, decline in physiologic reserve and function, hospitalization, and even death. The social and economic impact of frailty increased due to the higher healthcare costs and the medical resources. The intervention of early frailty detection can prevent its progression and delay the disability, ultimately improving the quality of life in the elderly population. This study aims to propose a frailty classification system based on gait data collected from an Inertial Measurement Unit (IMU) sensor with the utilization of the Deep Learning (DL) approach. The individual’s frailty status is classified as robust, pre-frail, or frail. A publicly available dataset of 163 participants was utilized to analyze the raw gait signals and find the most effective DL for extracting gait patterns for frailty classification. DeepConvLSTM model has shown effective performance on raw IMU gait data with a balanced accuracy, precision, recall, and F1-score of 91%. The results show that the proposed methodology successfully classifies the pre-frail individuals, which demonstrate its potential to enhance frailty detection and intervention in clinical settings. This ultimately provides an improved healthcare system and a quality of life in elderly populations.

Paper Citation

in Harvard Style

Amjad A., Szczęsna A. and Błaszczyszyn M. (2025). Deep Learning for Frailty Classification Using Raw Inertial Sensor Gait Data. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 311-317. DOI: 10.5220/0013127300003890

in Bibtex Style

@conference{icaart25,
author={Arslan Amjad and Agnieszka Szczęsna and Monika Błaszczyszyn},
title={Deep Learning for Frailty Classification Using Raw Inertial Sensor Gait Data},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={311-317},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013127300003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Deep Learning for Frailty Classification Using Raw Inertial Sensor Gait Data
SN - 978-989-758-737-5
AU - Amjad A.
AU - Szczęsna A.
AU - Błaszczyszyn M.
PY - 2025
SP - 311
EP - 317
DO - 10.5220/0013127300003890
PB - SciTePress