Home-based stroke rehabilitation robotics for the upper limb: User needs, design, and control
Bibliographic record
Abstract
With an aging rural population at risk for stroke and a stroke care system that is already struggling to meet its targets, Canada must develop technologies to improve access to and availability of stroke rehabilitation. Most stroke survivors experience arm impairments following stroke that continue into the chronic phase of stroke, but many lack access to therapy post-discharge. Rehabilitation robots could address this need, but more research needs to be done to ensure that they can safely and effectively meet stroke survivors’ needs in the home environment. In this work, the design of at-home upper limb rehabilitation robots is investigated. A scoping review of publications describing the design of at-home upper limb rehabilitation robots was conducted. The design features and the justifications for the designs were identified and analyzed, showing that most designs support a very narrow range of motions and activities and that stroke survivors were not involved in the design process. A study of stakeholder needs was conducted by interviewing stroke survivors and therapists about how rehabilitation robots could address their needs for at-home rehabilitation. Key design considerations were identified, especially the possibility of incorporating or simulating household items in the designs and the importance of monitoring the trunk and shoulder. Results from the stakeholder study were related to technical design requirements using the House of Quality method. A set of design priorities and targets were produced from this analysis. The importance of arm, trunk, and hand sensing, audiovisual feedback, and kinesthetic feedback were emphasized. A novel at-home upper limb rehabilitation robot, a cable robot that supports a user’s arm in a vertical planar workspace, was designed based on the identified priorities. The anticipated ability of the robot to meet user needs was estimated and is comparable to mature, commercialized devices. A novel teleoperation approach, Intent-Preserving Teleoperation, was developed and simulated. This approach uses force and velocity data to prevent the distortion in a therapist’s intended force delivery to a patient through a delayed communication channel. By advancing the understanding of stakeholder needs for at-home rehabilitation robots, more effective robots can be developed more efficiently.
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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 | 0.006 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".