Name
Abstract | ||
|---|---|---|
Since human bodies are good reflectors of wireless signals, human activities can be recognized by monitoring changes in WiFi signals. However, existing WiFi-based human activity recognition systems do not build models that can quantify the correlation between WiFi signal dynamics and human activities. In this paper, we propose a Channel State Information (CSI)-based human Activity Recognition and Monitoring system (CARM). CARM is based on two theoretical models. First, we propose a CSI-speed model that quantifies the relation between CSI dynamics and human movement speeds. Second, we propose a CSI-activity model that quantifies the relation between human movement speeds and human activities. Based on these two models, we implemented the CARM on commercial WiFi devices. Our experimental results show that the CARM achieves recognition accuracy of 96% and is robust to environmental changes. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/JSAC.2017.2679658 | IEEE Journal on Selected Areas in Communications |
Keywords | Field | DocType |
Wireless fidelity,Activity recognition,Hidden Markov models,OFDM,Feature extraction,Wireless communication,Monitoring | Activity recognition,Wireless,Monitoring system,Computer science,Feature extraction,Real-time computing,Theoretical models,Hidden Markov model,Orthogonal frequency-division multiplexing,Channel state information | Journal |
Volume | Issue | ISSN |
35 | 5 | 0733-8716 |
Citations | PageRank | References |
34 | 1.01 | 23 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Wang | 1 | 1458 | 82.69 |
| Alex X. Liu | 2 | 2727 | 174.92 |
| Muhammad Shahzad | 3 | 728 | 44.77 |
| Kang Ling | 4 | 202 | 7.71 |
| Sanglu Lu | 5 | 1380 | 144.07 |