Deep Learning-Based Fall Detection Using WiFi Channel State Information
Bibliographic record
Abstract
Falls have always been one of the major threats to the health and well-being of elderly people, particularly for those living alone. Both wearable and non-wearable fall detection systems have already been developed. However, the fall detection systems using WiFi channel state information (CSI) have attracted a significant interest from researchers due to their non-intrusive and low-cost nature. There are existing machine learning (ML) based fall detection systems using WiFi CSI; however, most systems trained with comprehensive datasets tend to achieve relatively lower accuracy compared to that of the systems trained with less inclusive datasets. To address these issues, we propose a novel, deep learning based fall detection technique. First, we implement different WiFi CSI collection tools and evaluate their potential for fall detection. To develop a highly accurate fall detection technique, we construct a comprehensive dataset, which consists of over 700 CSI samples including different types of falls and other daily activities, performed in four different indoor environments on and off the dominant paths. With this dataset, we then develop a deep learning based classifier using an image classification algorithm. The proposed technique, unlike the other fall detection systems, only requires down sampling and reshaping in pre-processing. The proposed fall detection system is evaluated with the constructed dataset, and it outperforms two other existing systems. It achieves over 96% accuracy for CSI collected in all four environments and 99% accuracy for CSI collected in certain combinations of the environments.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".