Developing a User-friendly System for Home-based Monitoring of Arm Use after Stroke
Bibliographic record
Abstract
Stroke rehabilitation faces challenges in providing real-time care outside clinical settings, as they require in-person supervision and lack personalized monitoring of continuous progression in stroke rehabilitation. Recent advances in wearable sensors (e.g., inertial measurement unit (IMU), electromyography (EMG)) offer avenues to longitudinally track arm use in the real-world setting, e.g. during activities of daily living at home. However, challenges remain in translating such technology mainly due to practical barriers in transferring the technical knowledge and skills required for operating the sensor/device to acquire data. The main objective of this project was to develop a user-friendly system that consists of: 1) “easy-to-don and-doff” wearable sensors, 2) “easy-to-use” graphical user interface (GUI) for data acquisition, and 3) “on-the-go” receiver unit for reliable wireless connection. To this end, we used Myo Armband (Thalmic Labs, CANADA), which is a consumer-grade bracelet sensor capable of capturing IMU data and EMG signals to assess movement and muscle activity that communicates via bluetooth. We developed a Python-based GUI that collects and displays real-time data visualization for the two Myo Armbands on the upper arm and forearm. This system has enabled real-time monitoring, reliable tracking, and scalable rehabilitation, enhancing accessibility, engagement, and recovery outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".