Combining low sampling frequency smartphone sensors and video for a Wearable Mobility Monitoring System
Bibliographic record
Abstract
<ns4:p>A proof-of-concept Wearable Mobility Monitoring System (WMMS) was developed to identify daily activities and provide environmental context, using integrated BlackBerry Smartphone low sensor and video data. Integrated accelerometer data were used to identify mobility changes-of-state (CoS) in real-time, trigger BlackBerry video capture at each CoS, and save activity outcomes on the Smartphone. System evaluation involved collecting WMMS output and (separate) camcorder video under realistic conditions for five able-bodied subjects. The subjects each performed a consecutive series of mobility tasks; including, walking, sitting, lying, stairs, ramps, elevator, bathroom activities, kitchen activities, dining activities and outdoor walking. Activity, timing and contextual information were obtained from the camcorder for comparison. Sensitivity results for sensor-based CoS identification were 97-100% for standing, sitting, lying and taking an elevator; 67-73% for walking-related CoS (stairs, ramps); 40-93% between walking and small movements (brushing teeth, etc.); and below 27% for daily living activities. False positives occurred in less than 12% of all activities, with less than 5% false positives for half the measures. Better classification results were achieved when using both acceleration features and Smartphone integrated video for all activities except sitting. The evaluation demonstrated that the WMMS algorithm and BlackBerry platform were effective for detecting mobility activities, even with low sampling rate sensors. The combined sensor and video analysis enhanced mobility task identification and contextual information.</ns4:p>
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 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".