Accelerometer-Triggered Electrical Stimulation for Reach and Grasp in Chronic Stroke Patients
Bibliographic record
Abstract
BACKGROUND: Electrical stimulation of the upper extremity may reduce impairment in patients following stroke. Stimulation triggered on demand combined with task practice may be an effective means of promoting recovery of function. OBJECTIVE: The authors investigated the feasibility of using accelerometer-controlled electrical stimulation for the elbow, wrist, and finger extensors to enable functional task practice in patients with chronic hemiparesis. METHODS: Following a 4-week baseline, participants received 2 weeks of cyclic stimulation exercise to elbow and forearm extensor muscles, followed by 10 weeks of triggered stimulation to practice functional reaching. Participants were reassessed 12 weeks later as well. Outcome measures were the Action Research Arm Test (ARAT), Modified Ashworth Scale (MAS), Canadian Occupational Performance Measure (COPM), Psychosocial Impact of Assistive Devices Scale (PIADS), and Use of Device Questionnaire (UDQ). RESULTS: Fifteen volunteers who had at least 45° of forward shoulder flexion and could initiate elbow extension and grasp completed the study. The ARAT score improved from 19 to 32 (P = .002); the MAS score for elbow, wrist, and finger flexor spasticity was reduced from 2 each to 1, 0, and 1 (P < .05); the COPM performance and satisfaction scores improved (P = .001); and the PIADS became positive for competence (P = .005), adaptability (P = .008), and self-esteem (P = .008). Gains were maintained 12 weeks later. CONCLUSIONS: Accelerometer-triggered electrical stimulation to augment task training for the hemiplegic arm is feasible and may improve functional ability and quality of life which may be maintained 12 weeks after treatment. A randomized trial design is required to evaluate efficacy and cost benefit.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".