HeadScan: A Wearable System for Radio-Based Sensing of Head and Mouth-Related Activities
Bibliographic record
Abstract
The popularity of wearables continues to rise. However, possible applications, and even their raw functionality are constrained by the types of sensors that are currently available. Accelerometers and gyroscopes struggle to capture complex user activities. Microphones and image sensors are more powerful but capture privacy sensitive information. Physiological sensors are obtrusive to users as they often require skin contact and must be placed at certain body positions to function. In contrast, radio-based sensing uses wireless radio signals to capture movements of different parts of the body, and therefore provides a contactless and privacy-preserving approach to detect and monitor human activities. In this paper, we contribute to the search for new sensing modalities for the next generation of wearable devices by exploring the feasibility of mobile radiobased human activity recognition. We believe radio-based sensing has the potential to fundamentally transform wearables as we currently know them. As the first step to achieve our vision, we have designed and developed HeadScan, a first-of-its-kind wearable for radio-based sensing of a number of human activities that involve head and mouth movements. HeadScan only requires a pair of small antennas placed on the shoulder and collar and one wearable unit worn on the arm or the belt of the user. Head- Scan uses the fine-grained CSI measurements extracted from radio signals and incorporates a novel signal processing pipeline that converts the raw CSI measurements into the targeted human activities. To examine the feasibility and performance of HeadScan, we have collected approximate 50.5 hours data from seven users. Our wide-ranging experiments include comparisons to a conventional skin-contact audio-based sensing approach to tracking the same set of head and mouth-related activities. Our experimental results highlight the enormous potential of our radio-based mobile sensing approach and provide guidance to future explorations.
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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 | 0.001 |
| 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".