Accessible In-Home Gait Assessment Using Spatiotemporal Neural Networks with Visual and Kinematic Data
Bibliographic record
Abstract
Gait analysis traditionally relied on controlled laboratory settings, limiting its practical use in non-clinical environments. This study proposes an accessible framework utilizing consumer electronics, combining data from an Apple Watch and a visual sensor system, to measure stride time (ST) across three walking speeds: slow, normal, and fast. Data was collected from eight participants in a semi-controlled setting designed to match real-world conditions. The machine learning framework, combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks, was utilized to analyze the multimodal sensor data and calculate ST. The system demonstrated strong agreement with an infrared marker-based optical motion capture system particularly at slower walking speeds. These findings underscore the feasibility of combining consumer-grade wearable and ambient sensors for accurate, accessible gait analysis in nonclinical settings.Clinical Relevance-This framework offers a cost-effective solution for gait analysis, reducing reliance on expensive clinical equipment. By utilizing consumer electronics, it provides a user-friendly and accessible alternative for individuals with mobility impairments, enabling regular assessments in non-clinical settings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".