Quantifying movement fluency in amputees in key functional tasks
Bibliographic record
Abstract
Abstract Objective. Sit-to-stand (STS) and sit-to-walk (STW) movements are key functional tasks to master following lower limb amputation. They are core to activities of daily living, enabling patients to regain independence. Physiotherapists assess movement fluency (hesitation and smoothness) by observing STS and STW however, this relies on extensive experience and lacks objectivity. This study aimed to establish objective, accessible and scalable quantitative measurements of movement fluency in amputees using instrumented movement analysis. Approach. 12 transfemoral amputees (six limited community and six community ambulators) and six typical individuals completed walking, STS and STW tasks. Movement fluency was assessed using published algorithms to obtain hesitation and smoothness in STS and STW. Main results. In STW, hesitation, and smoothness showed statistically significant differences among the three groups. Community ambulators were significantly less hesitant ( p = 0.009) and smoother ( p = 0.007) than the limited community ambulators, but significantly more hesitant ( p < 0.001) and less smooth ( p < 0.001) than typical individuals. In STS, the community ambulators were significantly smoother than the limited community ambulators ( p < 0.001), but not significantly different from typical individuals ( p = 0.68). Community ambulators walked significantly faster than limited community ambulators ( p < 0.001) but significantly slower compared to typical individuals ( p < 0.001). Significance. Assessment of movement after amputation is not just about walking speed. Other important functional tasks can differentiate amputees and therefore should be considered. An amputee must learn to master both the STS and STW tasks before they can independently walk. Quantifying movement fluency in functional tasks is important to understanding the restoration of function following limb loss, tracking rehabilitation, and classifying amputees. While the study’s small sample size reflects its feasibility design, findings support future research with larger cohorts. Subsequent studies should incorporate power calculations to improve generalisability.
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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".