EVALUATING THE DYNAMIC PERFORMANCE OF INTERFACIAL PRESSURE SENSORS AT A SIMULATED BODY-DEVICE INTERFACE
Bibliographic record
Abstract
BACKGROUND: Pressure sensing at the body-device interface can help assess the quality of fit and function of assistive devices during physical activities and movement such as walking and running. However, the dynamic performance of various pressure sensor configurations is not well established.
 OBJECTIVE(S): Two common commercially available thin-film pressure sensors were tested to determine the effects of clinically relevant setup configurations focusing on loading areas, interfacing elements (i.e. ‘puck’) and calibration methods.
 METHODOLOGY: Testing was performed using a customized universal testing machine to simulate dynamic, mobility relevant loads at the body-device interface. Sensor performance was evaluated by analyzing accuracy and hysteresis.
 FINDINGS: The results suggest that sensor calibration method has a significant effect on sensor performance although the difference is mitigated by using an elastomeric loading puck. Both sensors exhibited similar performance during dynamic testing that agree with accuracy and hysteresis values reported by manufacturers and in previous studies assessing mainly static and quasi-static conditions.
 CONCLUSION: These findings suggest that sensor performance under mobility relevant conditions may be adequately represented via static and quasi-testing testing. This is important since static testing is much easier to apply and reduces the burden on users to verify dynamic performance of sensors prior to clinical application. The authors also recommend using a load puck for dynamic testing conditions to achieve optimal performance.
 Layman's Abstract
 Pressure sensors can be used in prosthetics to provide clinicians with data about how well a device fits and functions. However, pressure sensors are unproven when it comes to use during activities such as walking or running. This study tested two common pressure sensors in a setup that applied forces similar to walking. These findings indicate that sensor calibration affects sensor accuracy. Accuracy can be improved by applying a small puck to the sensor to spread the load more evenly. With the puck, the performance of the sensors was found to be acceptable for potential use in clinical applications. These findings also show that dynamic testing of pressure sensors may not be needed prior to clinical usage. Instead, performance can be based on static testing which is easier to do.
 Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/36059/27891
 How To Cite: Hamilton M, Sivasambu H, Behdinan K, Andrysek J. Evaluating the dynamic performance of interfacial pressure sensors at a simulated body-device interface. Canadian Prosthetics & Orthotics Journal. 2021;Volume 4, Issue 1, No.4. https://doi.org/10.33137/cpoj.v4i1.36059
 Corresponding Author: Jan Andrysek, PhD,Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada.Email: jandrysek@hollandbloorview.caORCID: https://orcid.org/0000-0002-4976-1228
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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 | 0.001 | 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 | 0.001 |
| 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".