Video surveillance for near-fall detection at home
Bibliographic record
Abstract
This paper presents the feasibility of a low-cost video surveillance system that can be used at home to assess the fall risk of older adults. This is of paramount importance since fall is the greatest hazard for older adults. To detect early signs of mobility decline in older adults the system simply detects near-falls with machine learning as part of a fall prevention plan. A One-Class SVM was trained to combine spatiotemporal features from normal activities of daily living. The spatiotemporal features were extracted from a simplified skeleton fitted to the body based on a keypoint RCNN algorithm. Then the system was used to estimate normality scores to identify abnormal events. In practice, a near-fall will trigger a notification to document the fall risk probability. Our experimental results demonstrated that the One-Class SVM could successfully distinguish anomalies (near-falls) with a detection accuracy of 90%, specificity of 87.67% and sensitivity of 93.33% on a dataset of 55 videos (> 16000 frames) of simulated normal and abnormal activities in a realistic apartment-laboratory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".