Investigating the Effects of Mechanical Variables on the Performance of Resistive Bend Sensors Used in Digit Motion Tracking
Bibliographic record
Abstract
Resistive bend sensors are commonly used in data gloves to measure finger joint angles and are favored for their durability, flexibility, and affordability. However, their performance can be affected by mechanical factors, such as the range of motion (ROM) and speed, which have yet to be fully investigated. This study aimed to systematically explore the effects of ROM and speed on the performance of resistive bend sensors, focusing on four performance indices, i.e., sensitivity, repeatability, hysteresis, and sensor relaxation. Sensors from two manufacturers, Spectra Symbol and Flexpoint, were tested across six ROMs and seven speeds. Statistical analysis was performed to find the correlation between performance indices and ROM and speed, as well as the difference between two sensors. The results showed that the performance of the two sensors is affected by ROM and speed. ROM affected the sensitivity, repeatability, and relaxation indices; however, the results were not similar across the two sensors. Speed affected the sensitivity and relaxation, with differences in the observed effects between the two sensors. Hysteresis did not show any significant correlation with either ROM or speed. Finally, the sensor’s sensitivity was observed to be dependent on the direction of movement (flexion or extension). Our experimental results suggested that comprehensive and targeted experiments are required before integrating resistive bend sensors into data gloves for different applications.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".