140:10·L.Wang et al. Averaged signa c0. -Coarse alignment -Fine alignment 0.6 0.4 -0.2 0.2 0.2 -0. 0.4 0 0 02 0.40.60.8 0.20.40.6 100 200 300 Time(s) Time(s) Deviation(ms) (a)Fine-alignment-template selection (b)Fine alignment results (c)CDF of the time deviations in different alignment schemes Fig.8.Fine alignment results start of the ATC stage,instead of using a heuristic interval in the coarse-template.We observe that the smoothed SCG signal remains almost static before the ATC stage and starts to change drastically at the ATC stage.Thus, to estimate the start of the ATC stage,we first normalize the amplitude of the smoothed signal by dividing the samples by the maximum amplitude of the signal.We then estimate the first derivative of the smoothed signal S'(t)=ds(t)/dt using the expression S(t)S(t +m)-S(t),where we take the time difference m as four sample points(i.e.,40 ms at a sampling rate of 100 Hz).As shown in Figure 8(a),the first derivative of the SCG signal, S'(t),has a high amplitude at the start of ATC.Therefore,we use a threshold based scheme to detect the ATC start on the normalized SCG signal.We use the smoothed SCG signal between the ATC starting point and the RF as the fine-alignment-template,see Figure 8(a). The fine-template is used for aligning the heartbeat cycles in a testing continuous heartbeat sequence.We perform a cross-correlation between the fine-template and the testing sequence.Note that the fine-template should have a similar heart rate as the testing sequence,as it is selected based on the heart rate estimation. Therefore,by locating the peaks in the cross-correlation result,we can accurately align the starting point of the ATC stage of different heartbeat cycles.Figure 8(b)shows the aligned of fifteen heartbeat cycles collected over a period of three days for a user.We observe that our fine alignment scheme can precisely match the key features of the AO-RF interval.To evaluate the performance of the alignment scheme,we collected SCG signals from five users,each containing 100 heartbeat cycles.Figure 8(c)shows the CDF of alignment deviations for the heart rate estimation algorithm and the fine alignment algorithm.For the alignment achieved by the coarse-template,the average deviation is 45.23 ms,which is much larger than the average deviation of 9.02 ms from the fine alignment algorithm. 5 FEATURE EXTRACTION In this section,we focus on extracting features for user authentication from the SCG signals.Firstly,we preprocess the SCG signals to normalize both the amplitude and the length of the heartbeat signals.Secondly,we use the wavelet-based method to extract one set of feature vectors from each heartbeat cycle. 5.1 Normalization The normalization algorithm takes the aligned heartbeat signals and uses two steps to reduce the variations of the SCG signals.The first step is to reduce the variation of the SCG amplitude so that heartbeats collected under different conditions have comparable amplitudes.The amplitude of SCG signals depends on the angle between the mobile phone's y-axis and the chest of the user,the position of the mobile phone,and the pressure that the user applied to the phone when collecting the heartbeat signal.Our system allows the user to collect the SCG signals in slightly different ways.Therefore,the amplitudes of the SCG signals collected under different conditions are Proc.ACM Interact.Mob.Wearable Ubiquitous Technol.,Vol.2,No.3,Article 140.Publication date:September 2018.140:10 • L. Wang et al. 0 0.2 0.4 0.6 0.8 Time (s) -0.3 -0.2 -0.1 0 0.1 0.2 Acceleration (m/s 2 ) Averaged signal Derivative Threshold line Fine template (a) Fine-alignment-template selection 0 0.2 0.4 0.6 Time(s) -0.4 -0.2 0 0.2 0.4 Acceleration (m/s 2 ) (b) Fine alignment results 0 100 200 300 Deviation (ms) 0 0.2 0.4 0.6 0.8 1 CDF Coarse alignment Fine alignment (c) CDF of the time deviations in different alignment schemes Fig. 8. Fine alignment results start of the ATC stage, instead of using a heuristic interval in the coarse-template. We observe that the smoothed SCG signal remains almost static before the ATC stage and starts to change drastically at the ATC stage. Thus, to estimate the start of the ATC stage, we first normalize the amplitude of the smoothed signal by dividing the samples by the maximum amplitude of the signal. We then estimate the first derivative of the smoothed signal S ′ (t) = dS (t)/dt using the expression S (t) ≈ S (t +m) − S (t), where we take the time difference m as four sample points (i.e., 40 ms at a sampling rate of 100 Hz). As shown in Figure 8(a), the first derivative of the SCG signal, S ′ (t), has a high amplitude at the start of ATC. Therefore, we use a threshold based scheme to detect the ATC start on the normalized SCG signal. We use the smoothed SCG signal between the ATC starting point and the RF as the fine-alignment-template, see Figure 8(a). The fine-template is used for aligning the heartbeat cycles in a testing continuous heartbeat sequence. We perform a cross-correlation between the fine-template and the testing sequence. Note that the fine-template should have a similar heart rate as the testing sequence, as it is selected based on the heart rate estimation. Therefore, by locating the peaks in the cross-correlation result, we can accurately align the starting point of the ATC stage of different heartbeat cycles. Figure 8(b) shows the aligned of fifteen heartbeat cycles collected over a period of three days for a user. We observe that our fine alignment scheme can precisely match the key features of the AO-RF interval. To evaluate the performance of the alignment scheme, we collected SCG signals from five users, each containing 100 heartbeat cycles. Figure 8(c) shows the CDF of alignment deviations for the heart rate estimation algorithm and the fine alignment algorithm. For the alignment achieved by the coarse-template, the average deviation is 45.23 ms, which is much larger than the average deviation of 9.02 ms from the fine alignment algorithm. 5 FEATURE EXTRACTION In this section, we focus on extracting features for user authentication from the SCG signals. Firstly, we preprocess the SCG signals to normalize both the amplitude and the length of the heartbeat signals. Secondly, we use the wavelet-based method to extract one set of feature vectors from each heartbeat cycle. 5.1 Normalization The normalization algorithm takes the aligned heartbeat signals and uses two steps to reduce the variations of the SCG signals. The first step is to reduce the variation of the SCG amplitude so that heartbeats collected under different conditions have comparable amplitudes. The amplitude of SCG signals depends on the angle between the mobile phone’s y-axis and the chest of the user, the position of the mobile phone, and the pressure that the user applied to the phone when collecting the heartbeat signal. Our system allows the user to collect the SCG signals in slightly different ways. Therefore, the amplitudes of the SCG signals collected under different conditions are Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 2, No. 3, Article 140. Publication date: September 2018