Wearable Devices for Gait Analysis in Intelligent Healthcare
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this study, we review the role of wearable devices in tracking our daily locomotion. We discuss types of wearable devices that can be used, methods for gait analyses, and multiple healthcare-related applications aided by artificial intelligence. Impaired walking and locomotion are common resulting from injuries, degenerative pathologies, musculoskeletal disorders, and various neurological damages. Daily tracking and gait analysis are convenient and efficient approaches for monitoring human walking, where concreate and rich data can be obtained for examining our posture control mechanism during body movement and providing enhanced clinical pieces of evidence for diagnoses and treatments. Many sensors in wearable devices can help to record data of walking and running; spatiotemporal and kinematic variables can be further calculated in gait analysis. We report our previous works in gait analysis, discussing applications of wearable devices for detecting foot and ankle lesions, supporting surgeons in early diagnosis, and helping physicians with rehabilitation.
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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 | 0.001 | 0.004 |
| 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 it