Online Segmentation of Human Motion for Automated Rehabilitation Exercise Analysis
Bibliographic record
Abstract
To enable automated analysis of rehabilitation movements, an approach for accurately identifying and segmenting movement repetitions is required. This paper proposes an approach for online, automated segmentation and identification of movement segments from continuous time-series data of human movement, obtained from body-mounted inertial measurement units or from motion capture data. The proposed approach uses a two-stage identification and recognition process, based on velocity features and stochastic modeling of each motion to be identified. In the first stage, motion segment candidates are identified based on a characteristic sequence of velocity features such as velocity peaks and zero velocity crossings. In the second stage, hidden Markov models are used to accurately identify segment locations from the identified candidates. The proposed approach is capable of online segmentation and identification, enabling interactive feedback in rehabilitation applications. The approach is validated on 20 healthy subjects and four rehabilitation patients performing rehabilitation movements, achieving segmentation accuracy of 87% with user specific templates and 79%-83% accuracy with user-independent templates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".