Instrumented Upper-Body Brace for Computerized Training of Muscle Control
Bibliographic record
Abstract
Each year in the United States, over 2,000,000 individuals suffer from neuromuscular disorders that severely impair movement abilities.Physical therapy is the predominant option for rehabilitating motor function for these patients; however, traditional therapies often focus on physical training without greater cognitive engagement or leveraging of motor learning principles.As such, computerized interfaces for rehabilitation, such as virtual reality and robotics, are more promising given their natural approaches to motivate and provide enhanced feedback about performance while re-training motor skills.Our laboratory has prototyped an upperextremity brace device integrated with a virtual reality environment for isometric training of improved muscle-level control of the upperbody for persons with motor disability.This research platform includes a position-adjustable restrictive upper-extremity brace instrumented with sensors for skin-surface electromyography (EMG, measure muscle activity) to control virtual avatars and vibration motors for haptic guidance cues during training.The core objective of this research is to adapt the current brace design to better include instrumentation elements (EMG sensors, vibration motors) onboard the brace towards an embodiment of this device that is self-contained and with greater commercial potential.Specifically, this project will focus on building the next version of this brace system that allows for custom-placement of affordable (not high-end research-grade) commercial EMG (Myoware) sensors at locations personally fitted to each participant.The Myoware sensors will be embedded onto the current upper-body restrictive brace through modular attachments based on designs developed in SolidWorks as presented in this paper.The SolidWorks design utilizes sliding mechanisms, screws, springs, and clamps to make the modular attachment more user-friendly and position adaptable in three dimensions.Embedding Myoware sensors onto the brace design replaces the need to tape research-grade (Delsys) sensors onto each participant to ensure flush and consistent contact with each participant arm for robust EMG measurements and reliable transfer of haptic feedback.Overall, these improved design implementations will result in a version of this device that is more affordable, easier to use, more customizable to each user, and facilitates greater portability.The potential customers and stakeholders would include not only patients, but also clinical support staff and telehealth companies.This versatile, advanced system for computerized rehabilitation will be valuable to any communities of neuromuscular disorders affecting upper-body function that benefit from motor rehabilitation.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".