System Architecture Data collection and Feature extraction Identification pre-processing Gait cycle time 1.Model generation 1.CSI data collection Torso speed Training data Footstep size collection Leg speed Spectrogram signature 2.PCA denoising 2.Prediction SVM based prediction 3.Spectrogram generation 204000100 40600 0406082 10 7196
7/96 System Architecture Data collection and pre-processing Feature extraction - Gait cycle time - Torso speed - Footstep size - Leg speed - Spectrogram signature 1. CSI data collection 2. PCA denoising 3. Spectrogram generation Identification 1. Model generation 2. Prediction Training data collection SVM based prediction
How to deal with noisy signals? Signals from commercial WiFi n 75 devices are very noisy 60 Time (Seconds) 12 12.5 Original 75 Traditional filter based denoising approaches not work "wwwpylo- 11.5 12 12.5 Time (seconds) Low-pass filter Analysis based approach in our MobiCom 15 paper 11.5 12 12.5 Time (seconds) PCA 8/96
8/96 How to deal with noisy signals? § Signals from commercial WiFi devices are very noisy § Traditional filter based denoising approaches not work § We proposed a Principal Component Analysis based approach in our MobiCom 15 paper 11 11.5 12 12.5 60 65 70 75 CSI Time (seconds) Original 11 11.5 12 12.5 65 70 75 CSI Time (seconds) Low-pass filter 11 11.5 12 12.5 −10 −5 0 5 10 Time (seconds) CSI PCA