Methods for Classifying Physical Activities Using Accelerometer Data: A Scoping Review
Bibliographic record
Abstract
Abstract Accurate classification of physical activity from accelerometer data is critical for health research and large-scale population studies. However, the wide variety of computational methods used to derive activity types and intensity levels has led to inconsistencies in implementation, validation, and reproducibility. This scoping review aimed to identify and categorise methods used to classify physical activities using accelerometer data, with a particular focus on implementation, simplicity, validation strategies, and feasibility for application in large-scale datasets such as the All of Us Research Program. We searched PubMed, Web of Science, and SPORTDiscus for studies published between 2015 and 2025. Studies were included if they used accelerometer data to classify specific activities or general activity levels and reported a validation strategy. A total of 1,670 records were screened; 158 met the inclusion criteria. Data were extracted on study characteristics, classification methods, whether validation was performed, device use, specifications, and tool availability. Machine-learning techniques were the most frequently applied classification method (n = 81), followed by deep learning (n = 63), hybrid models (n = 23), rule-based or threshold approaches (n = 22), and unsupervised or other novel methods (n = 12). Walking (n = 97), sitting (n = 79), and standing (n = 68) were the most commonly studied activities. Most studies employed lab-based protocols and used k-fold or leave-one-subject-out validation. Only 16 studies provided public code or tools, and just a couple (n = 2) considered seasonality or population diversity. This review highlights substantial variation in activity classification methods and reporting practices. Open-source tool availability and validation in real-world conditions remain limited. There is a need for simpler, validated, and reproducible approaches, particularly for use in population-scale datasets like All of Us and the UK Biobank.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.235 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.042 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".