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Methods for classifying physical activities using accelerometer data: a scoping review.

Kiyan Sadeghi Janbahan¹, Osvaldo Espin-Garcia^2,3,4

¹Schulich School of Medicine and Dentistry, Western University, London, ON, Canada. ksadegh2@uwo.ca.

NPJ Digital Medicine

|May 6, 2026

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Classifying physical activity from accelerometer data is inconsistent across studies. This review highlights the need for simpler, validated, and reproducible methods for large health datasets.

Area of Science:

Health research
Wearable technology
Data science

Background:

Accurate physical activity classification from accelerometer data is crucial for health research and large-scale studies.
Current computational approaches lack consistency in implementation, validation, and reproducibility.
This variability poses challenges for large datasets like the All of Us Research Programme.

Purpose of the Study:

To identify and categorize methods for classifying physical activity from accelerometer data.
To emphasize implementation, simplicity, validation, and feasibility for large datasets.
To address inconsistencies in current physical activity classification research.

Main Methods:

Scoping review of studies from 2015-2025.

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Last Updated: May 8, 2026

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption

Published on: June 20, 2025

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

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Home-Based Monitor for Gait and Activity Analysis

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Searched PubMed, Web of Science, and SPORTDiscus databases.

Included studies classifying physical activity or activity levels from accelerometer data with a validation strategy.

Main Results:

158 studies met inclusion criteria out of 1851 records screened.
Machine learning (73 studies) was the most common approach, followed by deep learning (38) and hybrid models (27).
Walking, sitting, and standing were the most frequently studied activities. Most studies used lab-based validation, with limited public code availability (16 studies) and seasonality examination (2 studies).

Conclusions:

Significant variation exists in physical activity classification methods and reporting.
Limited availability of open-source tools and real-world validation hinders reproducibility.
There is a critical need for simpler, validated, and reproducible approaches for population-scale datasets.