Activities and Evaluations for Technology-Based Upper Extremity Rehabilitation
Bibliographic record
Abstract
Recent advances in projection and sensing have resulted in an increased adoption of virtual reality, video games, and interactive interfaces to improve patient compliance with rehabilitation programs. In this chapter, we describe the application of multi-touch tabletop surfaces to physical and occupational rehabilitation programs that are focused on the upper extremities. First, we detail the participatory design processes undertaken with local physical and occupational therapists to design and integrate a ‘patient-friendly' multi-touch tabletop system in their workplace. We then explore the design considerations that informed the development of a suite of sixteen multi-touch interactive activities. The design considerations highlighted the need for customization and flexibility in the software, as well as the importance of supporting a variety of activity types. We then detail the laboratory-based methods that were used to evaluate the efficacy of the activity interventions as well as our deployment of the system in a local rehabilitation hospital. Our evaluation, which employed both qualitative and quantitative components (i.e., the Intrinsic Motivation Inventory, semi-structured interviews, kinetics and kinematics recorded from motion trackers and an electromyogram recorder), determined that it is the design of activities, rather than the utilization of technology itself, that impacts the success of technology-assisted rehabilitation. The chapter concludes with a discussion of the implications of our system and its deployment.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".