Haptic feedback and human performance in a wearable sensor system
Bibliographic record
Abstract
Breathing is an unconscious and unheeded function occurs about 20,000 times per day and 10 million times per year by a healthy individual. Unlike other unconscious functions of the body, breathing can be controlled and regulated voluntarily. Breathing therapy was introduced to help people gradually refine their breathing patterns in terms of respiration rate and volume. It is very important for people to perform the exact instructions since otherwise it may cause lung problems. Therefore, the breath specialists and physicians try to provide instructions and feedback over multiple practice sessions. To overcome these limitations, this work proposes and investigates artificial tactile stimuli for providing instructions and feedback on performance of breathing exercises in real time. The proposed system comprises of a single wearable sensor that models the breathing functions from the interior/posterior motions of the subject's umbilical region integrated with a vibration motor to provide haptic biofeedback during breathing therapy. The results are investigated based on five different yogic breathing patterns with 10 healthy volunteers. Experimental tests have shown more than 40% improvement in users' performance in breathing therapy when applying haptic feedback. This shows a potential to enrich the quality of wearable and portable advisory-based systems through the touch channel.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".