ALSensing: Human Activity Recognition using WiFi based on Active Learning
Bibliographic record
Abstract
Over the past years, Human Activity Recognition (HAR) has shown its great value and has been further developed with the help of deep learning. However, existing HAR systems that use deep learning methods to achieve the ideal accuracy of recognition heavily rely on massive amounts of labeled training samples. Unfortunately, it requires considerable human effort and is unrealistic for real-life applications. In this paper, we propose a novel system, which combines active learning with WiFi-based HAR. The system is capable of building a good activities recognizer in HAR with a limited amount of labeled training samples. We thus call the system ALSensing. To the best of our knowledge, ALSensing is the first system to apply active learning to WiFi-based HAR. We implement ALSensing using commercial WiFi devices and evaluated it with realistic data in several different environments. Our experimental results show that ALSensing achieves 52.83% recognition accuracy using 3.7% training samples, 58.97% recognition accuracy using 15% training samples and the baseline predicted with the existing method achieves 62.19% recognition accuracy using 100% training samples. When the performance of ALSensing is similar to that of the baseline, the required labeled samples are much less than that of the baseline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".