Fall Detection Algorithm Using a Smart Wearable System for Remote Health Monitoring
Why this work is in the frame
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Bibliographic record
Abstract
Nowadays more people prefer to live independently, especially the elderly, leaving them prone to incidents that they might not be able to report.Falls, for instance, are responsible for over 3 million emergency hospitalizations for head injuries and hip fractures each year in the U.S. In addition, other cases often go unreported, leading to further complications including chronic disabilities and even fatality.Therefore, the detection of such incidents has become of urgent necessity.The purpose of this paper is to develop and propose a machine learning support vector classification (SVC) algorithm for fall detection using accelerometer, gyroscope, and magnetometer sensors embedded in a smart wearable system for remote health monitoring.The device is placed on the subject's wrist to collect data on various motion activities in real-time, such as walking, running, jogging, waving, and stair-climbing in addition to other static postures like standing, lying, and sitting.The constructed dataset comprises 30 subjects with over 1200 data frames.The model achieved an overall accuracy of 98.3% and a specificity of 98.2% in separating falls from other daily-life activities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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