On the Use of Ultra Wideband Radar and Stacked LSTM-RNN for at Home Fall Detection
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Bibliographic record
Abstract
Fail detection problem for smart home-care systems using an ultra wideband radar is considered in this paper. The goal is to identify the occurrence of fall from the radar return signals through a supervised learning approach. To this end, a new framework is proposed based on stacked long-short-term memory (LSTM) recurrent neural network to develop a robust method for feature extraction and classification of radar data of human daily activity. It is noted that the proposed method do not require heavy preprocessing on the data or feature engineering. It is known that LSTM networks are capable of capturing dependencies in time series data. In view of this, the radar time series data are directly fed into a stacked LSTM network for automatic feature extraction. Experiments are conducted on radar data collected from different subjects, when performing fall and non-fall activities. It is shown that the proposed method can provide a classification accuracy higher than that yielded by the other existing methods.
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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 | 0.000 | 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) | 0.000 | 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