Intelligent Fall-Risk Assessment Based on Gait Stability and Symmetry Among Older Adults Using Tri-Axial Accelerometry

Wei-Chih Lien; Congo Tak-Shing Ching; Zheng-Wei Lai; Hui-Min David Wang; Jhih-Siang Lin; Yen-Chang Huang; Feng-Huei Lin; Wen-Fong Wang

doi:10.3389/fbioe.2022.887269

Intelligent Fall-Risk Assessment Based on Gait Stability and Symmetry Among Older Adults Using Tri-Axial Accelerometry

Front Bioeng Biotechnol. 2022 May 13:10:887269. doi: 10.3389/fbioe.2022.887269. eCollection 2022.

Authors

Wei-Chih Lien^{1

2

3}, Congo Tak-Shing Ching^{3

4}, Zheng-Wei Lai⁵, Hui-Min David Wang^{3

4}, Jhih-Siang Lin⁵, Yen-Chang Huang⁵, Feng-Huei Lin^{3

6

7}, Wen-Fong Wang⁵

Affiliations

¹ Department of Physical Medicine and Rehabilitation, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
² Department of Physical Medicine and Rehabilitation, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
³ Ph.D. Program in Tissue Engineering and Regenerative Medicine, National Chung Hsing University, Taichung, Taiwan.
⁴ Graduate Institute of Biomedical Engineering, National Chung Hsing University, Taichung, Taiwan.
⁵ Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan.
⁶ Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan.
⁷ Institute of Biomedical Engineering and Nano-medicine, National Health Research Institutes, Zhunan, Miaoli, Taiwan.

Abstract

This study aimed to use the k-nearest neighbor (kNN) algorithm, which combines gait stability and symmetry derived from a normalized cross-correlation (NCC) analysis of acceleration signals from the bilateral ankles of older adults, to assess fall risk. Fifteen non-fallers and 12 recurrent fallers without clinically significant musculoskeletal and neurological diseases participated in the study. Sex, body mass index, previous falls, and the results of the 10 m walking test (10 MWT) were recorded. The acceleration of the five gait cycles from the midsection of each 10 MWT was used to calculate the unilateral NCC coefficients for gait stability and bilateral NCC coefficients for gait symmetry, and then kNN was applied for classifying non-fallers and recurrent fallers. The duration of the 10 MWT was longer among recurrent fallers than it was among non-fallers (p < 0.05). Since the gait signals were acquired from tri-axial accelerometry, the kNN F1 scores with the x-axis components were 92% for non-fallers and 89% for recurrent fallers, and the root sum of squares (RSS) of the signals was 95% for non-fallers and 94% for recurrent fallers. The kNN classification on gait stability and symmetry revealed good accuracy in terms of distinguishing non-fallers and recurrent fallers. Specifically, it was concluded that the RSS-based NCC coefficients can serve as effective gait features to assess the risk of falls.

Keywords: accelerometry; fall risk; gait; older adults; stability; symmetry.