A Low-Cost Adaptive Balance Training Platform for Stroke Patients: A Usability Study
Bibliographic record
Abstract
Stroke patients usually suffer from asymmetric posture due to hemi-paresis that can result in reduced postural controllability leading to a balance deficit. This deficit increases the risk of falls, which often makes them dependent on caregivers for community ambulation, thus deteriorating their quality of life. Conventional balance training involves rehabilitation exercises performed under physiotherapist's supervision, where the scarcity of trained professionals as well as the cost of clinic-based rehabilitation programs can deter stroke survivors from undergoing regular balance training. Thus, researchers have been exploring technology-assisted solutions, e.g., home-based virtual reality (VR) setup. In this paper, we developed a VR-based balance training (VBaT) platform, where VR-augmented user-interface using Nintendo Wii balance boardwas tested in a laboratory setting for its feasibility. The VBaT offered tasks of varying difficulties to the participants that adapted to individual performance capability during balance training. We performed a preliminaryusability study with 7 stroke survivors (post-stroke period > 6 months). Preliminary results indicate the potential of theVBaT system to cause improvement in overall average task performance over the course of training while using the VBaT. Thus the VBaT system is proposed to be a step toward an effective balance training platform for people with balance disorder.
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 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 | 0.000 |
| 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".