Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry.

ABSTRACT:

Background

Accurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods. Here, we developed a novel composite energy expenditure estimation model by combining an activity classification model with a stride specific energy expenditure model for walking, running, and cycling.

Methods

We first trained a supervised deep learning activity classification model using pooled data from available adult accelerometer datasets. The composite energy expendit

SUBMITTER: Lendt C 

PROVIDER: S-EPMC11389320 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature