Monitoring Arm Movements Post-Stroke for Applications in Rehabilitation and Home Settings
Bibliographic record
Abstract
Optimal recovery of arm function following stroke requires patients to perform a large number of functional arm movements in clinical therapy sessions, as well as at home. Technology to monitor adherence to this activity would be helpful to patients and clinicians. Current approaches to monitoring arm movements are limited because of challenges in distinguishing between functional and non-functional movements. Here, we present an Arm Rehabilitation Monitor (ARM), a device intended to make such measurements in an unobtrusive manner. The ARM device is based on a single Inertial Measurement Unit (IMU) worn on the wrist and uses machine learning techniques to interpret the resulting signals. We characterized the ability of the ARM to detect reaching actions in a functional assessment dataset (functional assessment tasks) and an Activities-of-Daily-Living (ADL) dataset (pizza-making and walking task) from 12 participants with stroke. The Convolutional Neural Network (CNN) and Random Forests (RF) classifiers had a Matthews Correlation Coefficient score of 0.59 and 0.58 when trained and tested on the functional dataset, 0.50 and 0.49 when trained and tested on the ADL dataset, and 0.37 and 0.36 when trained on the functional dataset and tested on the ADL dataset, respectively. The latter is the most relevant scenario for the intended application of training during a clinical visit for monitoring movements in the in-home setting. The classifiers showed good performance in estimating the time spent reaching and number of reaching gestures and showed low sensitivity to irrelevant arm movements produced during walking. We conclude that the ARM has sufficient accuracy and robustness to merit being used in preliminary studies to monitor arm activity in rehabilitation or home applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".