Activity Recognition with Wearable Accelerometers using Deep Convolutional Neural Network and the Effect of Sensor Placement
Bibliographic record
Abstract
Human activity recognition (HAR) has become ubiquitous in modern daily life, and thus requires robust classification algorithms. Accelerometers are the most commonly used sensor for HAR, but often provide an incomplete picture of activities due to their locality. Given a sensor network of accelerometers, we believe there are two main issues that need to be addressed: (i) developing a robust, end-to-end classification framework, and (ii) identifying the optimum number and placement of sensors. To address these issues, a convolutional neural network (CNN) is implemented, tuned, and tested for activity classification. Our evaluation shows that the proposed system outperforms a number of other classifiers with a perfect classification accuracy (100%). Next, we utilize the developed pipeline to investigate the impact of different combinations of sensors and analyze HAR accuracy with respect to location and number of sensors. Our results show that at least two accelerometers are needed to achieve perfect classification for daily activities, while an accelerometer placed on the ankle is most informative for near-perfect performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".