Automated Stride Detection from OpenPose Keypoints Using Handheld Smartphone Video
Bibliographic record
Abstract
Gait analysis is important for assessing neurological disorders, but existing methods require human assistance and specialized tools, which can be time-consuming and resource intensive. Healthcare providers would benefit from automated gait analysis. The first step in this process is detecting strides and identifying foot events. While many studies have focused on identifying strides, few have utilized handheld videos. In this study, walking gait was recorded using a handheld smartphone at 60Hz. Body keypoints were identified using the OpenPose - Body 25 pose estimation model, and a new algorithm was developed to identify the movement plane, foot events, and strides from the keypoints. The stride identification results were compared to ground truth foot events labeled through direct observation. Stride detection was accurate within 2 to 5 frames, demonstrating the viability of automated stride detection for smartphone-based motion analysis. This new approach can help improve gait analysis to be more efficient and accessible, especially for individuals who require remote monitoring.
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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 | 0.001 |
| 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) | 0.000 | 0.001 |
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".