Toward a Wearable Gait Lab for Quantitative Assessment of Musculoskeletal Function
Bibliographic record
Abstract
Understanding a person's musculoskeletal function, which involves assessing the mechanical forces exerted by individual muscles and the resulting joint torques, is crucial for comprehending movement mechanisms and designing effective training and rehabilitation strategies. In the case of neurologically impaired individuals like stroke survivors, the primary rehabilitation goal is to restore locomotion function, significantly impacting their quality of life. In clinical settings, swiftly measuring the forces produced by individual muscles in a quantitative and non-invasive manner is challenging. Musculoskeletal assessment often relies on fast, standardized observational tools for qualitative evaluations of endurance and functional capability. Alternatively, fully equipped laboratories with multiple sensors provide quantitative evaluations of musculoskeletal performance at the joint and muscle levels, but this comes at the cost of time and complexity. Unfortunately, neither approach efficiently combines simplicity, rapidity, and quantitative evidence of muscle strength, essential requirements in standard clinical rehabilitation. This work aims to obtain rapid and quantitative measures of musculoskeletal function by combining wearable sensors, advanced musculoskeletal modeling, and signal-processing techniques. Three studies systematically reveal key aspects for developing a smart wearable tool. Firstly, we introduce a fully automated muscle localization algorithm to eliminate manual labor in identifying muscle sites. We combine such techniques with an EMG-sensorized leg garment and an EMG-driven model for the estimation of dorsi-plantar flexion ankle torque during a variety of dynamic tasks performed by healthy participants. The pipeline is then enhanced to generalize across different anatomies and neuromuscular control strategies of healthy participants and post-stroke individuals. A novel EMG-equipped garment and improved muscle localization algorithm are introduced. Lastly, laboratory-based technologies are replaced with five IMU sensors, enabling a fully wearable technology for non-invasive estimation of musculoskeletal parameters. In conclusion, this dissertation demonstrates that wearable and automated technologies offer a viable alternative to standard laboratory techniques, potentially saving experimental time crucial in clinical settings. These technologies could facilitate the use of advanced EMG-driven modeling pipelines in clinics, as well as in recreational and occupational domains.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.007 | 0.004 |
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".