ularly transmitting a known preamble of OFDM symbols between each other.For each Tx-Rx antenna pair,the driver of our Intel 5300 WiFi NIC reports CSI values for Se=30 OFDM subcarriers of the 20 MHz WiFi Channel [24].This leads to 30 matrices with dimensions MR x Mr per CSI sample. 4.NOISE REMOVAL 10 500 1000 500 The CSI values provided by commodity WiFi NICs are Sample inherently noisy because of the frequent changes in internal (a)Original time series (b)Filtered time series CSI reference levels,transmit power levels,and transmis- Figure 2:Original and filtered CSI time series sion rates.To use CSI values for recognizing keystrokes,such noise must first be removed from the CSI time series.For while a user was repeatedly pressing a key.We observe from this,WiKey first passes the CSI time series from a low- this figure that all subcarriers show correlated variations in pass filter to remove high frequency noises.Unfortunately,a their time series when the user presses the keys.The sub simple low pass filter does not denoise the CSI values very ef- carriers that are closely spaced in frequency show identical ficiently.Although strict low-pass filtering can remove noise variations whereas the subcarriers that farther away in fre- further,it causes loss of useful information from the signal as quency show non-identical changes.Despite non-identical well.To extract useful signal from the noisy CSI time series. changes,a strong correlation still exists even across the sub- WiKey leverages our observation that the variations in the carriers that are far apart in frequency.WiKey leverages this CSI time series of all subcarriers due to the movements of correlation and calculates the principal components from all hands and fingers are correlated.Therefore,it applies Prin- CSI time series.It then chooses those principal components cipal Component Analysis(PCA)on the filtered subcarriers that represent the most common variations among all CSI to extract the signals that only contain variations caused by time series movements of hands.Next,we first describe the process of applying the low-pass filter on the CSI time series and then explain how Wikey extracts hand and finger movement sig- nal using our PCA based approach. 2000 200 000 4.1 Low Pass Filtering The frequency of variations caused due to the movements of hands and fingers lie at the low end of the spectrum while the frequency of the noise lies at the high end of the spectrum.To remove noise in such a situation,Butterworth low-pass filter is a natural choice which does not signific- 2000 4000.300080 antly distort the phase information in the signal and has a maximally flat amplitude response in the passband and thus does not distort the hand and finger movement signal much.WiKey applies the Butterworth filter on the CSI time series of all subcarriers in each TX-RX antenna pair so that every stream experiences similar effects of phase distortion and group delay introduced by the filter.Although this pro- 2000 20o0098.60 8000 cess helps in removing some high frequency noise,the noise (a)#1,2,3,4,5 (b)#5.10,15,20,25 is not completely eliminated because Butterworth filter has slightly slow fall off gain in the stopband. Figure 3:Correlated variations in subcarriers We observed experimentally that the frequencies of the variations in CSI time series due to hand and finger move- There are two main advantages of using PCA.First,PCA ments while typing approximately lie anywhere between 3Hz reduces the dimensionality of the CSI information obtained to 80 Hz.As we sample CSI values at a rate of F=2500 from the 30 subcarriers in each TX-RX stream,which is samples/s,we set the cut-off frequency we of the Butter- useful because using information from all subcarriers for rthfilter at:三学==≈02rad/s.Figure keystroke extraction and recognition significantly increases 2(a)shows the amplitudes of the unfiltered CSI waveform the computational complexity of the scheme.Consequently. of a keystroke and Figure 2(b)shows the resultant from the PCA automatically enables Wikey to obtain the signals that Butterworth filter.We observe that Butterworth filter suc- are representative of hand and finger movements,without cessfully removes most of the bursty noises from the CSI having to devise new techniques and define new parameters waveforms for selecting appropriate subcarriers for further processing. Second.PCA helps in removing noise from the signals by 4.2 PCA Based Filtering taking advantage of correlated varations in CSI time series We observed experimentally that the movements of hands of different subcarriers.It removes the uncorrelated noisy and fingers results in correlated changes in the CSI time components,which can not be removed through traditional series for each subcarrier in every transmit-receive antenna low pass filtering.This PCA based noise reduction is one pair.Figure 3 plots the amplitudes of CSI time series of 10 of the major reasons behind high keystroke extraction and different subcarriers for one transmit-receive antenna pair recognition accuracies of our scheme.ularly transmitting a known preamble of OFDM symbols between each other. For each Tx-Rx antenna pair, the driver of our Intel 5300 WiFi NIC reports CSI values for Sc = 30 OFDM subcarriers of the 20 MHz WiFi Channel [24]. This leads to 30 matrices with dimensions MR × MT per CSI sample. 4. NOISE REMOVAL The CSI values provided by commodity WiFi NICs are inherently noisy because of the frequent changes in internal CSI reference levels, transmit power levels, and transmis￾sion rates. To use CSI values for recognizing keystrokes, such noise must first be removed from the CSI time series. For this, WiKey first passes the CSI time series from a low￾pass filter to remove high frequency noises. Unfortunately, a simple low pass filter does not denoise the CSI values very ef- ficiently. Although strict low-pass filtering can remove noise further, it causes loss of useful information from the signal as well. To extract useful signal from the noisy CSI time series, WiKey leverages our observation that the variations in the CSI time series of all subcarriers due to the movements of hands and fingers are correlated. Therefore, it applies Prin￾cipal Component Analysis (PCA) on the filtered subcarriers to extract the signals that only contain variations caused by movements of hands. Next, we first describe the process of applying the low-pass filter on the CSI time series and then explain how WiKey extracts hand and finger movement sig￾nal using our PCA based approach. 4.1 Low Pass Filtering The frequency of variations caused due to the movements of hands and fingers lie at the low end of the spectrum while the frequency of the noise lies at the high end of the spectrum. To remove noise in such a situation, Butterworth low-pass filter is a natural choice which does not signific￾antly distort the phase information in the signal and has a maximally flat amplitude response in the passband and thus does not distort the hand and finger movement signal much. WiKey applies the Butterworth filter on the CSI time series of all subcarriers in each TX-RX antenna pair so that every stream experiences similar effects of phase distortion and group delay introduced by the filter. Although this pro￾cess helps in removing some high frequency noise, the noise is not completely eliminated because Butterworth filter has slightly slow fall off gain in the stopband. We observed experimentally that the frequencies of the variations in CSI time series due to hand and finger move￾ments while typing approximately lie anywhere between 3Hz to 80 Hz. As we sample CSI values at a rate of Fs = 2500 samples/s, we set the cut-off frequency ωc of the Butter￾worth filter at ωc = 2π∗f Fs = 2π∗80 2500 ≈ 0.2 rad/s. Figure 2(a) shows the amplitudes of the unfiltered CSI waveform of a keystroke and Figure 2(b) shows the resultant from the Butterworth filter. We observe that Butterworth filter suc￾cessfully removes most of the bursty noises from the CSI waveforms. 4.2 PCA Based Filtering We observed experimentally that the movements of hands and fingers results in correlated changes in the CSI time series for each subcarrier in every transmit-receive antenna pair. Figure 3 plots the amplitudes of CSI time series of 10 different subcarriers for one transmit-receive antenna pair 0 500 1000 1500 10 11 12 13 14 15 16 17 18 19 Sample Amplitude (a) Original time series 0 500 1000 1500 11 12 13 14 15 16 17 Sample Amplitude (b) Filtered time series Figure 2: Original and filtered CSI time series while a user was repeatedly pressing a key. We observe from this figure that all subcarriers show correlated variations in their time series when the user presses the keys. The sub￾carriers that are closely spaced in frequency show identical variations whereas the subcarriers that farther away in fre￾quency show non-identical changes. Despite non-identical changes, a strong correlation still exists even across the sub￾carriers that are far apart in frequency. WiKey leverages this correlation and calculates the principal components from all CSI time series. It then chooses those principal components that represent the most common variations among all CSI time series. 2000 4000 6000 8000 1.8 2 2.2 2.4 2.6 2000 4000 6000 8000 3 4 Absolute Value 0 2000 4000 6000 8000 7 8 9 0 2000 4000 6000 8000 9 10 11 12 13 0 2000 4000 6000 8000 12 14 16 Sample (a) # 1,2,3,4,5 2000 4000 6000 8000 12 14 16 2000 4000 6000 8000 12 14 16 0 2000 4000 6000 8000 9 10 0 2000 4000 6000 8000 18 20 22 0 2000 4000 6000 8000 2 2.5 3 Sample (b) # 5,10,15,20,25 Figure 3: Correlated variations in subcarriers There are two main advantages of using PCA. First, PCA reduces the dimensionality of the CSI information obtained from the 30 subcarriers in each TX-RX stream, which is useful because using information from all subcarriers for keystroke extraction and recognition significantly increases the computational complexity of the scheme. Consequently, PCA automatically enables WiKey to obtain the signals that are representative of hand and finger movements, without having to devise new techniques and define new parameters for selecting appropriate subcarriers for further processing. Second, PCA helps in removing noise from the signals by taking advantage of correlated varations in CSI time series of different subcarriers. It removes the uncorrelated noisy components, which can not be removed through traditional low pass filtering. This PCA based noise reduction is one of the major reasons behind high keystroke extraction and recognition accuracies of our scheme