A Game-Based Mechatronic Device for Digital Rehabilitation of Hand Function After a Stroke: Design, Prototyping, and Feasibility Study
Bibliographic record
Abstract
Background: This paper presents an easy-to-use, affordable robotic manipulandum device (RMD) equipped with smart monitoring and assistive technologies to engage in game-based exercise and repetitive task practice. The RMD has been designed to enhance a wide range of fine motor manual dexterity skills, including thumb, finger, and wrist movements. By focusing on finger and hand functions, it extends its utility beyond basic reaching or object transfer movements. Various interchangeable 3D-printed therapy handles of different shapes and sizes can be easily attached to the RMD drive shaft. These handle movements can be used to engage with numerous affordable, commercially available computer games, allowing patients to practice tasks that involve varying movement amplitudes, speeds, precision, and cognitive challenges. Additionally, the device is capable of automatically recording and storing the patient's real-time performance data on any given computer, integrating assessment into treatment. Objective: A pilot study was conducted with 5 patients with stroke to examine the feasibility and benefits of a 6-week game-based exercise program using the proposed device. Methods: A feasibility study was conducted with 5 participants. Data were collected using the computer game-based upper extremity assessment of manual dexterity and Wolf Motor Function Test (WMFT) before and after the intervention lasting 6 weeks. Results: The pilot study demonstrated that clients' expectations related to manual dexterity were met. The average improvement in the functional ability score of the WMFT was 14 (SD 3) points, with all participants exceeding the minimal clinically important difference. The average reduction in total time was 30 (SD 14) seconds, with 4 of 5 participants surpassing the minimal clinically important difference. For the computer game-based upper extremity assessment, the average improvement in success rate was 23% (SD 12%), and the average decrease in response time was 105 (SD 44) milliseconds. Conclusions: Findings revealed acceptable, engaging, game-based, and task-oriented training with a high level of compliance. Substantial improvements from pre- to postintervention were observed using the WMFT and assessments of manual dexterity.
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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 | 0.001 |
| 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 | 0.000 |
| 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".