The performance of a machine learning model in predicting accelerometer-derived walking speed.

ABSTRACT:

Background

Obtaining long-term measurements of walking speed in large-scale studies remains challenging. The aim of this study was to develop and evaluate the performance of a machine learning classifier in predicting slow (≤4 km/h), moderate (4.1-5.4 km/h), and brisk (≥5.5 km/h) walking speeds in adults based on dual and single accelerometer set-ups.

Methods

Twenty-four adults (mean age [SD, range] 36.1 [11.9, 23-62] years) participated in the study. Two tri-axial accelerometers positioned on the thigh and low back were used to record body movements. A measuring wheel with a speedometer along with video recording were used to define and record consecutive 5-min periods with the three walking speeds and jogging during conditions resembling free-living. In addition, we includ

SUBMITTER: Logacjov A 

PROVIDER: S-EPMC11804687 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature