Table 4.
Comparison of recognition accuracy of systems using COTS Wi-Fi devices.
| Reference/ Sensing Metric |
Signal Processing (SP) | Learning Algorithm | Application | Number of Gestures | Recognition Accuracy |
|---|---|---|---|---|---|
| Wigest [34]/ RSSI |
Wavelet Filter; FFT, DWT; Threshold based signal extraction; |
Pattern Matching |
Hand Gesture Recognition: Hand movements with mobile device. |
7 hand gestures |
87.5%/96% (1 AP/3 AP’s) |
| WiGer [36]/ CSI |
Butterworth low pass filter |
Segmentation: multi-level wavelet decomposition algorithm and the short-time energy algorithm, DTW |
Hand gesture recognition |
7 hand gestures |
97.28%, 91.8%, 95.5%, 94.4% and 91% (Scenario 1 to 5) |
| WiCatch [37]/ CSI |
MUSIC algorithm | SVM | Two hand moving trajectories recognition |
9 hand gesture | 95% |
| WiFinger [33]/ RSSI and CSI |
Butterworth filter, Wavelet based denoising and PCA |
DTW | Finger gesture recognition |
8 finger gestures |
76%(RSSI) and 95% (CSI) |
| WiKey [15]/ CSI |
Low pass filter, PCA, DWT Shape features | DTW | Keystroke recognition |
37 keys | 77.4% to 93.4% |
| Mudra [38]/ CSI |
Thresholding | Stretch limited DTW | Mudra recognition |
9 finger gestures |
96% |
| SignFi [29]/ CSI |
Without SP | CNN | Sign language gesture recognition |
276 gestures |
95.72%, 93.98%, and 92.21% for lab 276, home 276 and lab + home 276 environments respectively |
| SignFi [29]/ CSI |
With SP—Multiple Linear Regression | CNN | Sign language gesture recognition |
276 gestures |
98.01%, 98.91%, 94.81%, and 86.66% for lab 276, home 276, lab + home 276 and lab 150 environments respectively |
| HOS-Re (Present work) | Without SP Cumulant Features | SVM | Sign language gesture recognition |
276 gestures |
97.84%, 98.26%, 96.34%, and 96.23% for lab 276, home 276, lab + home 276 and lab 150 environments respectively |