Abstract
Prior research has shown the potential of device-free WiFi sensing for human activity recognition. In this paper, we show for the first time WiFi signals can also be used to uniquely identify people. There is strong evidence that suggests that all humans have a unique gait. An individual's gait will thus create unique perturbations in the WiFi spectrum. We propose a system called WiFi-ID that analyses the channel state information to extract unique features that are representative of the walking style of that individual and thus allow us to uniquely identify that person. We implement WiFi-ID on commercial off-the-shelf devices. We conduct extensive experiments to demonstrate that our system can uniquely identify people with average accuracy of 93% to 77% from a group of 2 to 6 people, respectively. We envisage that this technology can find many applications in small office or smart home settings.
| Original language | English |
|---|---|
| Title of host publication | 2016 International Conference on Distributed Computing in Sensor Systems (DCOSS) |
| Publisher | IEEE |
| Pages | 75-82 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781509014606 |
| ISBN (Print) | 9781509014613 |
| DOIs | |
| Publication status | Published - 26 May 2016 |
Keywords
- feature extraction
- gait analysis
- wireless LAN
- WiFi-ID
- human identification
- WiFi signals
- human gait
- WiFi spectrum
- channel state information
- walking style
- person identification
- commercial off-the-shelf devices
- small office settings
- smart home settings
- IEEE 802.11 Standard
- Feature extraction
- OFDM
- Data mining
- Legged locomotion
- Wireless communication
- Receivers
- Channal State Information
- Human Identification
- WiFi