Comparing Accelerometer Processing Metrics and Hyperparameter Optimization for Physical Activity Classification Accuracy Using Machine Learning Methods
Bibliographic record
Abstract
Background: Physical activity (PA) is a crucial factor in maintaining good health and preventing chronic diseases. However, accurately measuring PA is challenging. Euclidean Norm Minus One (ENMO), ActiGraph Counts, and Monitor-Independent Movement Summary (MIMS) units are processing metrics used to classify PA through accelerometry, but they employ different methods to calculate activity levels. This study aimed to compare ENMO, ActiGraph Counts, and MIMS accelerometer metrics using machine learning algorithms. Methods: Data from a smartphone accelerometer were collected from 50 participants who held the smartphone in their right hand while completing six activities. The data were used to generate ENMO, ActiGraph Counts, and MIMS acceleration metrics. Random Forest, K-Nearest Neighbor, and Support Vector Machine algorithms were applied to the data to classify PA into different levels of activity intensity and types. The algorithms’ performance was evaluated using various metrics such as accuracy, precision, and recall. Results: The results showed that both the Random Forest and K-Nearest Neighbor algorithms performed well, achieving above 80% accuracy in classifying PA into different intensity levels and types. Both the ENMO and MIMS metrics proved more accurate than ActiGraph Counts in classifying moderate to vigorous PA. Conclusions: This study provides evidence that both ENMO and MIMS metrics can accurately measure PA with accelerometry, and machine learning algorithms can classify the activity into different intensity levels. These metrics and methods are valuable tools for monitoring PA and understanding the relationship between PA and health outcomes.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".