Comparison of Wearable Sensor-based Fall Event Detection by 1-D and 2-D Convolutional Neural Networks
Bibliographic record
Abstract
Monitoring of the elderly people for their safety and well-being is crucial to help them to age independently in their own homes. Ubiquitous wearable sensors such as accelerometers and gyroscope have been used for monitoring activities of daily life (ADL) and fall. Traditional machine learning algorithms are currently replaced by the emerging deep learning methods to detect fall events and classify ADL. In this paper, fall event detection abilities of two recently proposed CNN architectures for fall event detection are compared. These two architectures have different receptive fields and therefore, discover different number of features for fall event detection. Performance of 1-D and 2-D versions of each of these architectures are compared using the data from two 3-axis accelerometers (large and small dynamic range), and a 3-axis gyroscopes available in a public dataset. 1-D CNN uses raw time signal while 2-D CNN uses the spectrograms of the raw signal as input. Effect of noise on the fall event detection using small dynamic range accelerometer is also studied. It is observed that 2-D CNN provides better fall event detection than 1-D CNN for all the three sensors and fall event detection is slightly better with accelerometers than with gyroscopes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".