Automatic Detection of Fatigued Gait Patterns in Older Adults: An Intelligent Portable Device Integrating Force and Inertial Measurements with Machine Learning.

IF 3 2区 医学 Q3 ENGINEERING, BIOMEDICAL Annals of Biomedical Engineering Pub Date : 2024-08-13 DOI:10.1007/s10439-024-03603-z
Guoxin Zhang, Tommy Tung-Ho Hong, Li Li, Ming Zhang
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Abstract

Purpose: This study aimed to assess the feasibility of early detection of fatigued gait patterns for older adults through the development of a smart portable device.

Methods: The smart device incorporated seven force sensors and a single inertial measurement unit (IMU) to measure regional plantar forces and foot kinematics. Data were collected from 18 older adults walking briskly on a treadmill for 60 min. The optimal feature set for each recognition model was determined using forward sequential feature selection in a wrapper fashion through fivefold cross-validation. The recognition model was selected from four machine learning models through leave-one-subject-out cross-validation.

Results: Five selected characteristics that best represented the state of fatigue included impulse at the medial and lateral arches (increased, p = 0.002 and p < 0.001), contact angle and rotation range of angle in the sagittal plane (increased, p < 0.001), and the variability of the resultant swing angular acceleration (decreased, p < 0.001). The detection accuracy based on the dual signal source of IMU and plantar force was 99%, higher than the 95% accuracy based on the single source. The intelligent portable device demonstrated excellent generalization (ranging from 93 to 100%), real-time performance (2.79 ms), and portability (32 g).

Conclusion: The proposed smart device can detect fatigue patterns with high precision and in real time.

Significance: The application of this device possesses the potential to reduce the injury risk for older adults related to fatigue during gait.

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老年人疲劳步态的自动检测:将力和惯性测量与机器学习相结合的智能便携设备
目的:本研究旨在通过开发一种智能便携式设备,评估早期检测老年人疲劳步态的可行性:该智能设备包含七个力传感器和一个惯性测量单元(IMU),用于测量区域性足底力和足部运动学。收集了 18 位老年人在跑步机上快步行走 60 分钟的数据。每个识别模型的最佳特征集都是通过五重交叉验证,使用前向顺序特征选择法确定的。识别模型是从四个机器学习模型中通过 "留出一个被试 "交叉验证选出的:结果:所选的五个最能代表疲劳状态的特征包括内侧和外侧足弓的冲量(增加,p = 0.002 和 p 结论:所选的五个最能代表疲劳状态的特征包括内侧和外侧足弓的冲量:建议的智能设备可以高精度地实时检测疲劳模式:意义:该设备的应用有可能降低老年人因步态疲劳而受伤的风险。
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来源期刊
Annals of Biomedical Engineering
Annals of Biomedical Engineering 工程技术-工程:生物医学
CiteScore
7.50
自引率
15.80%
发文量
212
审稿时长
3 months
期刊介绍: Annals of Biomedical Engineering is an official journal of the Biomedical Engineering Society, publishing original articles in the major fields of bioengineering and biomedical engineering. The Annals is an interdisciplinary and international journal with the aim to highlight integrated approaches to the solutions of biological and biomedical problems.
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