Wearable strain gauge-based technology measures manual tactile forces during the activities of daily living
Bibliographic record
Abstract
INTRODUCTION: Current methods of determining applied forces in the hand rely on grip dynamometers or force-measurement gloves which are limited in their ability to isolate individual finger forces and interfere with the sense of touch. The objective of this study was to develop an improved force measurement system that could be used during various activities of daily living. METHODS: Custom-made strain gauge sensors were secured to the fingernail of four fingers and two middle phalanges and calibrated to measure hand forces in eight healthy individuals during five activities of daily living. RESULTS: These sensors were capable of measuring forces as small as 0.17 N and did not saturate at high force tasks around 15 N, which is within the envelope of forces experienced during daily life. Preliminary data demonstrate the ability of these tactile sensors to reliably distinguish which fingers/segments were used in various tasks. CONCLUSIONS: Until now, there has been no method for real-time unobtrusive monitoring of force exposure during the tasks of daily life. The system used in this study provides a new type of low-cost wearable technology to monitor forces in the hands without interfering with the contact surface of the hand.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".