Unlock with Your Heart:Heartbeat-based Authentication on Commercial Mobile Phones.140:11 50 0 50 40 05 30 30 30 20 20 12 10 10 10 。4落列 -05 -05 102030 4050 1020304050 1020304050 (a)Distance calculated through DTW (b)Correlation of the raw SCG sequence (c)Correlation of the DWT features Fig.9.Distance for features extracted from five users'heartbeat signals with 10 heartbeats for each user different from each other.To verify this,we ask five users to repeat the data collection process,ie.,press the phone on the chest and then release,for more than 100 times.The angle between the mobile phone's y-axis and the chest is inconsistent,as the users cannot precisely repeat the action.We observe that standard deviation of the maximum acceleration for each heartbeat cycle is larger than 0.0217 m/s2,which is about one-tenth of the average amplitude of the AO peak.To remove this effect,we normalize the SCG signals of each heartbeat cycle by dividing with the maximum amplitude of the cycle. The second step is to normalize the heartbeat duration so that all heartbeat signals have the same length Remember that consecutive heartbeats may have different intervals.However,we observe that the AO-RF stage has small time-variations as shown in Figure 5(c).Therefore,most time variations come from the stages after the RF stage.As we have precisely aligned the starting point of the heartbeat cycle,the ATC stage,in Section 4.3,we pad zeros at the end of each heartbeat cycle to guarantee the same duration.This will introduce little interference,as the amplitudes of the SCG signals are quite small after the RF stage,see Figure 4.In this way,we pad all heartbeat cycles into the same length(e.g..128 points)that can accommodate the longest heartbeat cycle. 5.2 Wavelet-based Feature Extraction The feature extraction process needs to retain the characteristics of the user's heartbeats and remove irrelevant noises.Existing systems have proposed different feature extraction schemes.First,there are ECG-based [6,28,34, 50]and radio-based systems [39]that extract features based on the interval between different heartbeat stages. However,this scheme is not applicable to SCG signals because the variations in the amplitude of SCG lead to unreliable heartbeat stage identifications. Second,one of the common approaches for waveform matching is to use the Dynamic Time Warping(DTW) algorithm that calculates the distance between two waveforms [48,61].However,the DTW algorithm may move the peaks in one waveform by a short time offset to match with peaks in the other waveform.Therefore,DTW ignores the timing difference in heartbeat motion stages and only compares the amplitude of the SCG peaks.This leads to a high false positive rate because the timing differences between heartbeat stages are ignored.Figure 9(a)shows the Euclidean distance between SCG signals of five different users(with 10 heartbeat samples for each user)calculated through DTW.A smaller distance means two heartbeat signals are more similar to each other.While the samples from the same person always have the smallest distances to each other(red squares), we observe that samples from different users also have very small DTW distances.Therefore,the DTW distance may falsely recognize samples from an attacker as those from the authorized user. Third,it is also possible to use the raw time sequence of the SCG as a feature vector.However,the raw SCG sequences are noisy.The noises in SCG may come from the respiration,body and hand movements during the Proc.ACM Interact.Mob.Wearable Ubiquitous Technol.,Vol.2.No.3,Article 140.Publication date:September 2018.Unlock with Your Heart: Heartbeat-based Authentication on Commercial Mobile Phones • 140:11 10 20 30 40 50 10 20 30 40 50 10 12 14 16 18 20 (a) Distance calculated through DTW 10 20 30 40 50 10 20 30 40 50 -0.5 0 0.5 1 (b) Correlation of the raw SCG sequence 10 20 30 40 50 10 20 30 40 50 -0.5 0 0.5 1 (c) Correlation of the DWT features Fig. 9. Distance for features extracted from five users’ heartbeat signals with 10 heartbeats for each user different from each other. To verify this, we ask five users to repeat the data collection process, i.e., press the phone on the chest and then release, for more than 100 times. The angle between the mobile phone’s y-axis and the chest is inconsistent, as the users cannot precisely repeat the action. We observe that standard deviation of the maximum acceleration for each heartbeat cycle is larger than 0.0217 m/s 2 , which is about one-tenth of the average amplitude of the AO peak. To remove this effect, we normalize the SCG signals of each heartbeat cycle by dividing with the maximum amplitude of the cycle. The second step is to normalize the heartbeat duration so that all heartbeat signals have the same length. Remember that consecutive heartbeats may have different intervals. However, we observe that the AO-RF stage has small time-variations as shown in Figure 5(c). Therefore, most time variations come from the stages after the RF stage. As we have precisely aligned the starting point of the heartbeat cycle, the ATC stage, in Section 4.3, we pad zeros at the end of each heartbeat cycle to guarantee the same duration. This will introduce little interference, as the amplitudes of the SCG signals are quite small after the RF stage, see Figure 4. In this way, we pad all heartbeat cycles into the same length (e.g., 128 points) that can accommodate the longest heartbeat cycle. 5.2 Wavelet-based Feature Extraction The feature extraction process needs to retain the characteristics of the user’s heartbeats and remove irrelevant noises. Existing systems have proposed different feature extraction schemes. First, there are ECG-based [6, 28, 34, 50] and radio-based systems [39] that extract features based on the interval between different heartbeat stages. However, this scheme is not applicable to SCG signals because the variations in the amplitude of SCG lead to unreliable heartbeat stage identifications. Second, one of the common approaches for waveform matching is to use the Dynamic Time Warping (DTW) algorithm that calculates the distance between two waveforms [48, 61]. However, the DTW algorithm may move the peaks in one waveform by a short time offset to match with peaks in the other waveform. Therefore, DTW ignores the timing difference in heartbeat motion stages and only compares the amplitude of the SCG peaks. This leads to a high false positive rate because the timing differences between heartbeat stages are ignored. Figure 9(a) shows the Euclidean distance between SCG signals of five different users (with 10 heartbeat samples for each user) calculated through DTW. A smaller distance means two heartbeat signals are more similar to each other. While the samples from the same person always have the smallest distances to each other (red squares), we observe that samples from different users also have very small DTW distances. Therefore, the DTW distance may falsely recognize samples from an attacker as those from the authorized user. Third, it is also possible to use the raw time sequence of the SCG as a feature vector. However, the raw SCG sequences are noisy. The noises in SCG may come from the respiration, body and hand movements during the Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 2, No. 3, Article 140. Publication date: September 2018