Contact Force Estimation from Raw Photoplethysmogram Signal
Bibliographic record
Abstract
Commercial smartwatches and fitness trackers are integrating increasingly advanced physiological sensors. For optimal performance, such devices need to be firmly coupled to the body, yet also remain comfortable when worn for extended periods of time. Existing solutions for measuring the contact force in order to ensure it is in an optimal tightness range typically depend on direct force measurement, but this adds hardware, and therefore cost, to the devices. This paper presents a novel method for estimating contact force by using only an optical heart rate sensor, as already found in many wearable devices. Initial tests indicate that the proposed method can estimate contact force with a mean absolute error of 0.36N, on par with FSRs. This new approach has the potential to expand the utility of existing sensors for both researchers and end-users, with anticipated applications not only in optimizing physiological sensing, but also in haptic information delivery.
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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 | 0.000 |
| 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".