Abstract
Objective. We evaluate the stride segmentation performance of the Adaptive Empirical Pattern Transformation (ADEPT) for subsecond-level accelerometry data collected in the free-living environment using a wrist-worn sensor. Approach. We substantially expand the scope of the existing ADEPT pattern-matching algorithm. Methods are applied to subsecond-level accelerometry data collected continuously for 4 weeks in 45 participants, including 30 arthritis and 15 control patients. We estimate the daily walking cadence for each participant and quantify its association with SF-36 quality of life measures. Main results. We provide free, open-source software to segment individual walking strides in subsecond-level accelerometry data. Walking cadence is significantly associated with the role physical score reported via SF-36 after adjusting for age, gender, weight and height. Significance. Methods provide automatic, precise walking stride segmentation, which allows estimation of walking cadence from free-living wrist-worn accelerometry data. Results provide new evidence of associations between free-living walking parameters and health outcomes.
Original language | English (US) |
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Article number | 065006 |
Journal | Physiological Measurement |
Volume | 42 |
Issue number | 6 |
DOIs | |
State | Published - Jun 2021 |
Keywords
- accelerometry
- actigraphy
- cadence
- digital health
- gait
- walking segmentation
- wearable sensor
ASJC Scopus subject areas
- Physiology (medical)
- Biophysics
- Physiology
- Biomedical Engineering