Force Resistance Training in Hand Grasp and Arm Therapy: Feasibility of a Low-Cost Videogame Controller
Bibliographic record
Abstract
OBJECTIVE: To design and evaluate a low-cost gaming station that supports force resistance training in pediatric arm/hand grasp therapies through mainstream videogame play. METHODS: The gaming station was developed through an iterative participatory design process and includes a force feedback game controller (Novint Falcon), custom grips, arm/wrist supports, and software to interface with mainstream games and manage difficulty settings in the controller. The station was tested for usability and feasibility with six therapists and six children with cerebral palsy, 7-16 years of age, attending weekly therapy sessions over 12 weeks. Pre- and post-assessments of perceived performance and satisfaction on self-identified goals were measured on the Canadian Occupational Performance Measure (COPM). RESULTS: The gaming station was considered highly usable by therapists with a score of 76.7 (standard deviation [SD] = 6.1) on the System Usability Scale. Overall, children enjoyed the games, achieved high repetition rates for wrist extensions and arm movements, and all made clinically significant progress on therapy goals. Increases of 3.13 (SD = 1.69) on the performance scale and 2.97 (SD = 0.98) on the satisfaction scale were reported on the COPM. Conclusiion: In-clinic force resistance training for development of upper limb functional capacities is feasible using low-cost video game components adapted to therapy through a participatory design process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".