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Extracting actigraphy-based walking features with structured functional principal components.

Verena Werkmann¹, Nancy W Glynn², Jaroslaw Harezlak¹

¹School of Public Health, Department of Epidemiology and Biostatistics, Indiana University, Bloomington, IN, United States of America.

Physiological Measurement

|July 19, 2024

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Researchers analyzed walking patterns using accelerometry data to identify health indicators in older adults. Specific walking features correlated with age, BMI, and physical performance, suggesting walking patterns may reveal early signs of disease.

Area of Science:

Gerontology and Geriatric Medicine
Biomedical Engineering
Epidemiology

Background:

Walking is a primary physical activity, and its features may reflect an individual's physical health.
Understanding walking patterns can provide insights into health status, particularly in aging populations.

Purpose of the Study:

To extract subject-specific and subject-spectrum-specific walking features from accelerometry data.
To investigate the association between extracted walking features and health indicators like age, BMI, and physical performance.
To determine if walking patterns can serve as a marker for subclinical stages of somatic diseases.

Main Methods:

Utilized accelerometry data (ActiGraph GT3X+) from 48 older adults (mean age 78.7 years).
Applied structured functional principal component analysis (SFPCA) to raw walking signals after transforming data to the frequency domain using local Fast Fourier Transform.

Keywords:

accelerometry data functional data analysis functional principal component analysis multilevel functional data

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Analyzed subject-specific and subject-spectrum-specific features for associations with age and physical performance measures.

Main Results:

SFPCA identified 5 subject-specific and 19 subject-spectrum-specific features, explaining over 85% of data variation.
Higher acceleration magnitude at cadence correlated with younger age, lower BMI, faster cadence, and better physical performance.
Lower acceleration magnitude at cadence and higher magnitudes at cadence multiples (2.5, 3.5) were linked to older age and higher blood pressure.

Conclusions:

Extracted walking features using SFPCA are meaningfully associated with health indicators and age.
Individual walking patterns can potentially indicate subclinical stages of somatic diseases.
This approach offers a novel method for assessing health through everyday physical activity in older adults.