Variability-based assessment of assisted gait using a multi-sensor instrumented cane
Bibliographic record
Abstract
The biomechanics of walking with assistive canes are not well understood clinically, despite their long history. Canes are also often misused or not used at all once taken home, despite the known benefits of proper use. To help clinicians and users evaluate and monitor cane use at home and in the community, a multi-sensor instrumented cane and interpretable gait performance metrics have been proposed. This study investigated the effects of an instrumented cane on gait speed, stride interval, and gait variability in 15 healthy individuals. Participants were asked to walk for 10 min under three different conditions: normal walking (as a control), walking with plantar flexion of the ankle, and walking with dorsiflexion of the ankle. The results showed that relying solely on speed-related gait features may not be sufficient to detect gait changes caused by both the use of a cane and the simulated gait conditions. To gain a more comprehensive understanding of gait performance, additional gait parameters based on gait variability extracted from the user's motion and/or the device's motion are necessary. Specifically, the detrended fluctuation analysis (DFA) exponent of stride-to-stride fluctuations can be used to differentiate between changes in walking and modifications in cane use, while other measures based on mean values, such as gait speed and stride interval, cannot. These results suggest that the abundance of information acquired from the inertial sensor-based instrumented cane can be used for automated gait analysis and gait recognition to observe and comprehend an individual's walking performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 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".