Machine and Deep Learning for Detection of Moderate-to-Vigorous Physical Activity From Accelerometer Data: Systematic Scoping Review.

ABSTRACT:

Background

Accurate monitoring of moderate-to-vigorous physical activity (MVPA) is critical for advancing public health research and personalized interventions. Traditional accelerometry methods, reliant on regression-derived intensity cut points, exhibit significant misclassification errors and poor generalizability to the free-living environment. Recent advancements in machine learning (ML) and deep learning (DL) offer promising alternatives for automated MVPA detection.

Objective

This scoping review synthesizes evidence on ML and DL techniques for MVPA estimation and prediction using accelerometer data, focusing on performance, algorithm bias, sensor configurations, and translational potential.

Methods

Following PRISMA-ScR (Preferred Reporting Items for Systematic

SUBMITTER: Zi Y 

PROVIDER: S-EPMC12828321 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature