A wearable sensor system for rehabilitation apllications
Bibliographic record
Abstract
In this paper an easy-to-use, wearable sensor system capable of deciphering upper-limb based functional tasks associated with stroke rehabilitation is introduced. Such a system can assist the therapist with monitoring a patient's progress during rehabilitation, and hence can increase the efficiency of the rehabilitation process. The developed system provides quantitative, real-time feedback of a user's functional activity. The system is designed to detect the successful completion of functional tasks that involve the grasping, movement, and subsequent release of an object. The developed system consists of an Inertial Measurement Unit (IMU) attached to a band, embedded with force sensitive resistors for extracting Force Myography (FMG) data. The band is wrapped around the user's forearm for the purpose of detecting the onset of a grasp or release with the use of a Neural Network for data classification. Upon detection of a grasp and subsequent release, the device calculates the distance achieved during the motion using data captured from the attached IMU as a measure of task quality. The system's ability to detect the completion of three functional tasks was evaluated with nine healthy volunteers. The system achieved an average accuracy of 92.67%. Experimental results are presented and discussed.
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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) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".