Unlock with Your Heart:Heartbeat-based Authentication on Commercial Mobile Phones.140:7 0.012 2.5 0.05 -Volunteer A -Volunteer A -Volunteer A 0.01 Volunteer B Volunteer E 0.04 Volunteer B Volunteer C Volunteer C Volunteer C 密0.008 Volunteer D Volunteer D Volunteer D 0.006 Volunt rE Volunteer E Voluntee g0.04 0.02 0.002 在0.01 .400 -200 0 200 400 3 -50 0 50 Deviation(ms) Ratio (AO/RF) Deviation(ms) (a)Deviation of heartbeat interval from the (b)Ratio of AO amplitude to RF Amplitude (c)Deviation of AO-RF interval from the mean mean value value Fig.5.Variations in the SCG signal 3.3 Characteristics of the SCG Signal By looking at the SCG waveforms,we have the following observations that lead to the possibility of using the SCG signal for authentication: First,the SCG signals of different people go through the same seven stages,but have different signal patterns in terms of amplitudes of the corresponding peaks and intervals between peaks.Figure 3(b)and Figure 3(c)show two SCG samples of one heartbeat cycle from two volunteers.While both volunteers have similar heart rates(73 BPM and 71 BPM,respectively),the two SCG patterns have distinctive features.For example,the amplitudes of the AO peaks for the two volunteers are quite different.Such difference in heartbeat motion comes from the differences in the size,position and shape of the heart [38].Therefore,the heartbeat motion patterns contain unique biometric features of the given user [22]. Second,the SCG signals of the same user are consistent over time.Figure 4 shows five consecutive heartbeat patterns of two volunteers.While there are small variations in the signals,we observe that the heartbeat patterns from the same person are consistent for consecutive heartbeat cycles.Furthermore,with heartbeat patterns collected across three months and with different clothes,we find that heartbeat patterns of the same user are quite stable.Therefore,the SCG signal can potentially serve as a consistent identity for the user. 4 HEARTBEAT SEGMENTATION AND ALIGNMENT In this section,we describe the heartbeat segmentation and alignment process,in which the continuous heartbeat signals are divided into individual heartbeat cycles.High precision signal alignment is vital to heartbeat- based authentication systems.This is because a misaligned heartbeat signal will lead to incorrect positioning of the different heartbeat stages.Consequently,such incorrect positioning will lead to errors in user authentication. However,due to the variances in both the amplitude and timing of the SCG signals,it is challenging to precisely align the heartbeat signals. 4.1 Variations in the SCG Signal While human heartbeats are repetitive motions,ECG-based experiments show that heartbeats are not perfectly periodical [2,3,27,45].Therefore,the SCG signals also have variations in both the amplitude and timing of the peaks corresponding to different heart motion stages. First,human heartbeat rates are not stable.There are intrinsic Heartbeat Rate Variability(HRV)in SCG signals [42].Figure 5(a)shows the Probability Density Function(PDF)of the deviation in time intervals between two normal heartbeats for five volunteers sitting on the chair.The ground truth values are obtained by manually selecting the auto-correlation peaks in the SCG signals.We observe that the standard deviation of heartbeat interval is 46ms,which is consistent with results from ECG signals [42].Thus,the duration of heartbeat cycle 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 • 140:7 -400 -200 0 200 400 Deviation (ms) 0 0.002 0.004 0.006 0.008 0.01 0.012 Probability Density Volunteer A Volunteer B Volunteer C Volunteer D Volunteer E (a) Deviation of heartbeat interval from the mean value 0123 Ratio (AO/RF) 0 0.5 1 1.5 2 2.5 Probability Density Volunteer A Volunteer B Volunteer C Volunteer D Volunteer E (b) Ratio of AO amplitude to RF Amplitude -50 0 50 Deviation (ms) 0 0.01 0.02 0.03 0.04 0.05 Probability Density Volunteer A Volunteer B Volunteer C Volunteer D Volunteer E (c) Deviation of AO-RF interval from the mean value Fig. 5. Variations in the SCG signal 3.3 Characteristics of the SCG Signal By looking at the SCG waveforms, we have the following observations that lead to the possibility of using the SCG signal for authentication: First, the SCG signals of different people go through the same seven stages, but have different signal patterns in terms of amplitudes of the corresponding peaks and intervals between peaks. Figure 3(b) and Figure 3(c) show two SCG samples of one heartbeat cycle from two volunteers. While both volunteers have similar heart rates (73 BPM and 71 BPM, respectively), the two SCG patterns have distinctive features. For example, the amplitudes of the AO peaks for the two volunteers are quite different. Such difference in heartbeat motion comes from the differences in the size, position and shape of the heart [38]. Therefore, the heartbeat motion patterns contain unique biometric features of the given user [22]. Second, the SCG signals of the same user are consistent over time. Figure 4 shows five consecutive heartbeat patterns of two volunteers. While there are small variations in the signals, we observe that the heartbeat patterns from the same person are consistent for consecutive heartbeat cycles. Furthermore, with heartbeat patterns collected across three months and with different clothes, we find that heartbeat patterns of the same user are quite stable. Therefore, the SCG signal can potentially serve as a consistent identity for the user. 4 HEARTBEAT SEGMENTATION AND ALIGNMENT In this section, we describe the heartbeat segmentation and alignment process, in which the continuous heartbeat signals are divided into individual heartbeat cycles. High precision signal alignment is vital to heartbeat￾based authentication systems. This is because a misaligned heartbeat signal will lead to incorrect positioning of the different heartbeat stages. Consequently, such incorrect positioning will lead to errors in user authentication. However, due to the variances in both the amplitude and timing of the SCG signals, it is challenging to precisely align the heartbeat signals. 4.1 Variations in the SCG Signal While human heartbeats are repetitive motions, ECG-based experiments show that heartbeats are not perfectly periodical [2, 3, 27, 45]. Therefore, the SCG signals also have variations in both the amplitude and timing of the peaks corresponding to different heart motion stages. First, human heartbeat rates are not stable. There are intrinsic Heartbeat Rate Variability (HRV) in SCG signals [42]. Figure 5(a) shows the Probability Density Function (PDF) of the deviation in time intervals between two normal heartbeats for five volunteers sitting on the chair. The ground truth values are obtained by manually selecting the auto-correlation peaks in the SCG signals. We observe that the standard deviation of heartbeat interval is 46ms, which is consistent with results from ECG signals [42]. Thus, the duration of heartbeat cycle Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 2, No. 3, Article 140. Publication date: September 2018