Physical Frailty Prediction Using Cane Usage Characteristics during Walking

This study aimed to determine the characteristics of accelerations and angular velocities obtained by an inertial measurement unit (IMU) attached to a cane between older people with and without physical frailty. Community-dwelling older people walked at a comfortable speed using a cane with a built-in IMU. Physical frailty was assessed using exercise-related items extracted from the Kihon Check List. The efficacy of five machine learning models in distinguishing older people with physical frailty was investigated. This study included 48 older people, of which 24 were frail and 24 were not. Compared with the non-frail participants, the older people with physical frailty had a small root mean square value in the vertical and anteroposterior directions and angular velocity in the anteroposterior direction (<i>p</i> < 0.001, r = 0.36; <i>p</i> < 0.001, r = 0.29; <i>p</i> < 0.001, r = 0.30, respectively) and a large mean power frequency value in the vertical direction (<i>p</i> = 0.042, r = 0.18). The decision tree model could most effectively classify physical frailty, with an accuracy, F1 score, and area under the curve of 78.6%, 91.8%, and 0.81, respectively. The characteristics of IMU-attached cane usage by older adults with physical frailty can be utilized to effectively evaluate and determine physical frailty in their usual environments.

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Publication Details

Journal
Sensors
Published
2024-10-28
DOI
https://doi.org/10.3390/s24216910
Citations
3
Primary Topic
Frailty in Older Adults
Type
article
Field-Weighted Citation Impact
1.03

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article

Physical Frailty Prediction Using Cane Usage Characteristics during Walking

Takaaki Chin, Haruki Toda
3 citations
Sensors
Frailty in Older Adults
1.03
article

Physical Frailty Prediction Using Cane Usage Characteristics during Walking

Takaaki Chin, Haruki Toda
article en
3 citations

Abstract

This study aimed to determine the characteristics of accelerations and angular velocities obtained by an inertial measurement unit (IMU) attached to a cane between older people with and without physical frailty. Community-dwelling older people walked at a comfortable speed using a cane with a built-in IMU. Physical frailty was assessed using exercise-related items extracted from the Kihon Check List. The efficacy of five machine learning models in distinguishing older people with physical frailty was investigated. This study included 48 older people, of which 24 were frail and 24 were not. Compared with the non-frail participants, the older people with physical frailty had a small root mean square value in the vertical and anteroposterior directions and angular velocity in the anteroposterior direction (<i>p</i> < 0.001, r = 0.36; <i>p</i> < 0.001, r = 0.29; <i>p</i> < 0.001, r = 0.30, respectively) and a large mean power frequency value in the vertical direction (<i>p</i> = 0.042, r = 0.18). The decision tree model could most effectively classify physical frailty, with an accuracy, F1 score, and area under the curve of 78.6%, 91.8%, and 0.81, respectively. The characteristics of IMU-attached cane usage by older adults with physical frailty can be utilized to effectively evaluate and determine physical frailty in their usual environments.

SensorsVol. 24(21)
Hyogo Prefectural Institute of Technology (JP), Hyogo Social Welfare Corporation (JP)
Japan Society for the Promotion of Science
Sustainable cities and communities
Openalex Percentile: Top 23%
Frailty in Older Adults
1.03
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Physical Frailty Prediction Using Cane Usage Characteristics during Walking — Takaaki Chin, Haruki Toda · Sensors (2024) | TGRS Research Map | TGRS