Key result
The k-nearest neighbor algorithm using root sum of squares of tri-axial accelerometry signals achieved 94% overall accuracy in classifying older adults as recurrent fallers or non-fallers.
Why the study?
The study aimed to evaluate whether a k-nearest neighbor algorithm combining gait stability and symmetry from bilateral ankle tri-axial accelerometry could assess fall risk in older adults.
Does a kNN algorithm using gait stability and symmetry from tri-axial accelerometry accurately classify fall risk in older adults?
Cross-Sectional (n=27)
Yes
Does a kNN algorithm using gait stability and symmetry from tri-axial accelerometry accurately classify fall risk in older adults?
RSS-based NCC coefficients from tri-axial accelerometry combined with kNN classification can effectively assess fall risk in older adults.
Answers are grounded in this paper and cite their sources.
Exploratory gait metrics may support fall risk screening in older adults; leaves open prospective validation before clinical use.
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.
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Lien et al. (2022) conducted a cross-sectional in Fall risk (n=27). k-nearest neighbor (kNN) algorithm using root sum of squares (RSS) of tri-axial accelerometry signals vs. x-axis components of accelerometry signals (x-kNN) was evaluated on Classification accuracy of recurrent fallers vs non-fallers. The k-nearest neighbor algorithm using root sum of squares of tri-axial accelerometry signals achieved 94% overall accuracy in classifying older adults as recurrent fallers or non-fallers.
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