Characterization of Knee and Gait Features From a Wearable Tele-Health Monitoring System
Bibliographic record
Abstract
Mobility is crucial for healthy aging. Any disruption to mobility can affect mental, physical and social health, and socio-economic independence. Therefore, studies in gait and lower-joint functionality with respect to different demographic features will play a vital role in maintaining good mobility. In this study, we analyzed a gait database from 70 healthy subjects (18–86 years) constructed using our custom-built multi-sensor-based wearable tele-health monitoring system. The purpose was to extract and use the most informative features for classifying knee joint and gait characteristics of the subjects with respect to their age, body mass index – BMI, and sex. Four supervised machine learning algorithms: partial least square-discriminant analysis (PLS-DA), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were used to classify the subjects. The features that significantly contributed to all classifications are knee angle, quadriceps muscle pressure adjacent to the knee joint, rotational energy (mediolateral and vertical), acceleration energy (mediolateral), cross-sample entropy (anteroposterior-mediolateral), knee angle variability, symmetry of swing and stance phase, and walk ratio. Classification accuracies of all four methods were ~89%, 83%, 81%, 86% for age, 90%, 80%, 83%, 86% for BMI, and 97%, 97%, 96%, 97% for sex, respectively. PLS-DA had the best classification performance for all three categories which makes it preferable for these kinds of analyses. Thus, our knee and gait monitoring system coupled with an efficient machine learning tool can be exploited for real-time evaluation and early diagnoses of mobility disabilities, health assessment, and monitoring the need for interventions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".