A Multi-source Unsupervised Domain Adaptation Method for Wearable Sensor based Human Activity Recognition
Bibliographic record
Abstract
Human Activity Recognition (HAR) refers to recognizing a human's ongoing actions through sensor data. At present, one of the main problems faced by Human Activity Recognition is that different subjects, devices and wearing positions can cause inconsistent sensor data distribution. When a classification model trained using some labeled dataset is used to classify a new unlabeled data with different distributions, there will be a significant performance loss. However, it is difficult to annotate manually sensor data for new subjects. Prior works applying unsupervised domain adaptation methods to solve this problem only used a single source domain. However, in practice, it is common to have multiple labeled source domains. Inspired by a work in the field of computer vision, we propose an unsupervised domain adaptation method for human activity recognition using multiple source domains. Experimental results on a commonly used public HAR dataset show that our model can effectively alleviate the performance loss caused by inconsistent distributions. Moreover, compared with the single-source domain adaptation, the multi-source domain adaptation method can improve the accuracy further.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".