ALOHA: Leveraging Additional Information to Learn Robust Representations for Human Activity Recognition
Bibliographic record
Abstract
Human Activity Recognition using wearable sensors has applications in health monitoring, entertainment, and industrial settings. However, the performance of Human Activity Recognition models in real-life settings is usually lower than in laboratory settings due to the reduced quantity and quality of the sensors available in the former. Here, we propose using a suitable shared representation space to incorporate the information of additional sensors available during training time to address these limitations. We evaluate two representation spaces: one created using Feature Agglomeration and the other using Uniform Manifold Approximation and Projection (UMAP) under three conditions to evaluate their performance and robustness to noise: clean data, Gaussian noise, and Magnitude Warping noise using three datasets: Opportunity, Cooking, and PAMAP2. Our results consistently show that the representation spaces enhances performance relative to the conventional single-sensor method. The UMAP approach outperforms Feature Agglomeration, achieving up to a $14 \%$ improvement in the F1-Score metric when using clean data. In the presence of Gaussian noise, the UMAP representation space not only improves classification performance but also exhibits resilience to noise in the Opportunity and PAMAP2 datasets. While the UMAP method exhibits lower robustness to noise in the Cooking dataset, it still achieves the highest performance. When experimenting with Magnitude Warping noise, the UMAP representation space shows varying levels of robustness across datasets but still enhances performance to some extent. Using shared representations, we leverage the higher number and quality of sensors available in laboratory settings for training HAR models, while releasing the usual requirement of using the same number of sensors at the final deployment.
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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 | 0.000 | 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".