Name
Abstract | ||
|---|---|---|
With the rapid population ageing and increase of the elderly who live alone, there is a growing demand for intelligent monitoring, especially fall detection systems. In this paper, based on received signal strength (RSS) and machine learning algorithm, a fall detection method is proposed. It using multi-domain features, including time-domain and wavelet-domain, and Boost algorithm trains a model to discriminate fall and other actions, such as, sit, stand and squat. The experimental results show that the proposed method can identify falls well. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/SmartIoT49966.2020.00054 | 2020 IEEE International Conference on Smart Internet of Things (SmartIoT) |
Keywords | DocType | ISBN |
fall detection,RF signal,multi-domain features,XGBoost | Conference | 978-1-7281-6515-8 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Wen | 1 | 0 | 0.34 |
| Zhiyong Yang | 2 | 0 | 0.34 |
| Lei Jin | 3 | 60 | 10.34 |