140:8·L.Wang et al.. Generate Coarse Template Coarse Alignment SCG Sequence Linear Locate Prune Cross Detect Heart Rate Interpolation All Peaks Noisy Peaks Correlation Correlation Peaks Fig.6.Heart Rate Estimation Scheme could be changing by as much as 1/20 of the cycle length since a normal heartbeat lasts for about one second at a heart rate of 60 BPM. Second,the peak amplitude in the SCG signal varies significantly.Figure 5(b)shows the PDF for the ratio of the amplitude of the AO peak to the RF peak in the same heartbeat cycle.These two peaks are the most prominent features in the SCG signal.Different persons have different AO to RF ratios,as shown in Figure 4.The standard deviations of AO to RF ratio is larger than 0.25 for all volunteers.This implies that the AO to RF ratio for the same person also varies significantly,e.g.in consecutive heartbeat cycles,either the AO or the RF peak could be the highest peak in the cycle,see Figure 4(b).Therefore,it is challenging to identify the AO and RF peaks using a small number of heartbeat cycles.Existing systems use hints from other measurements,such as the photoplethysmogram(PPG)[59],to help identify the AO peak.However,our system only has the SCG signals as the reference to perform the segmentation. Fortunately,we observe that the time interval between the AO stage and RF stage is relatively stable.Figure 5(c)shows the PDF of the deviation in the time interval between the AO and the RF peak.The standard deviation of the AO-RF interval is 9.48 ms,which is much smaller than that of the heartbeat interval.This implies that the ratio of the AO-RF interval to the heartbeat interval also changes significantly,as the AO-RF interval is stable and the heartbeat interval is unstable.We further verified that the AO-RF intervals are stable under different states. We collect SCG signals when users finish exercising,recline on the sofa and lie on the bed.While the heart rates are significantly higher in the exercising state,the standard deviation of the AO-RF interval is still small(i.e., 11.2 ms).The standard deviations of AO-RF interval for the reclining and lying states are 8.25 ms and 5.86 ms, respectively. Based on the above observations,we choose to use the interval between the ATC stage and the RF stage as the reference for heartbeat segmentation and alignment.We choose the ATC-RF interval due to two reasons. First,the ATC-RF interval contains the two highest peaks in the SCG signal,i.e.,AO and RF,that can be easily identified.Second,the time interval between AO and RF has smaller variations than other parts of the heartbeat cycle.We design a two-step process to divide and align the heartbeat using the signals in the reference interval as follows. 4.2 Heart Rate Estimation Given a new SCG sequence,the first step is to use a heart rate estimation algorithm,as shown in Figure 6,to measure the heart rates.To estimate the heart rates,we first use a linear interpolation algorithm to normalize the accelerometer readings to a standard sampling rate (e.g.,100 Hz).This step ensures that our system can work on mobile phones that have different sampling rates for the accelerometer. The second step of heart rate estimation is to derive a coarse-template of the reference ATC-RF interval from the SCG signals.To identify the ATC-RF interval,we first locate all the peaks(local maximum points)in an SCG sequence with a two-second duration.We assume that the heart rates of the user are between 50 BPM and 120 BPM.Therefore,there is at least one full heartbeat cycle in the two-second SCG signal.We sort the local maximum points by their amplitudes as the labels shown in Figure 7(a).We then perform a pruning algorithm to remove noisy peaks.Starting from the highest peaks,we add the peaks into a candidate set one-by-one in the descending order of their amplitudes.If the current peak is within a time interval of r to one of the candidate peaks in the set, Proc.ACM Interact.Mob.Wearable Ubiquitous Technol.,Vol.2,No.3,Article 140.Publication date:September 2018.140:8 • L. Wang et al. SCG Sequence Generate Coarse Template Linear Interpolation Prune Noisy Peaks Locate All Peaks Coarse Alignment Detect Correlation Peaks Cross Correlation Heart Rate Fig. 6. Heart Rate Estimation Scheme could be changing by as much as 1/20 of the cycle length since a normal heartbeat lasts for about one second at a heart rate of 60 BPM. Second, the peak amplitude in the SCG signal varies significantly. Figure 5(b) shows the PDF for the ratio of the amplitude of the AO peak to the RF peak in the same heartbeat cycle. These two peaks are the most prominent features in the SCG signal. Different persons have different AO to RF ratios, as shown in Figure 4. The standard deviations of AO to RF ratio is larger than 0.25 for all volunteers. This implies that the AO to RF ratio for the same person also varies significantly, e.g., in consecutive heartbeat cycles, either the AO or the RF peak could be the highest peak in the cycle, see Figure 4(b). Therefore, it is challenging to identify the AO and RF peaks using a small number of heartbeat cycles. Existing systems use hints from other measurements, such as the photoplethysmogram (PPG) [59], to help identify the AO peak. However, our system only has the SCG signals as the reference to perform the segmentation. Fortunately, we observe that the time interval between the AO stage and RF stage is relatively stable. Figure 5(c) shows the PDF of the deviation in the time interval between the AO and the RF peak. The standard deviation of the AO-RF interval is 9.48 ms, which is much smaller than that of the heartbeat interval. This implies that the ratio of the AO-RF interval to the heartbeat interval also changes significantly, as the AO-RF interval is stable and the heartbeat interval is unstable. We further verified that the AO-RF intervals are stable under different states. We collect SCG signals when users finish exercising, recline on the sofa and lie on the bed. While the heart rates are significantly higher in the exercising state, the standard deviation of the AO-RF interval is still small (i.e., 11.2 ms). The standard deviations of AO-RF interval for the reclining and lying states are 8.25 ms and 5.86 ms, respectively. Based on the above observations, we choose to use the interval between the ATC stage and the RF stage as the reference for heartbeat segmentation and alignment. We choose the ATC-RF interval due to two reasons. First, the ATC-RF interval contains the two highest peaks in the SCG signal, i.e., AO and RF, that can be easily identified. Second, the time interval between AO and RF has smaller variations than other parts of the heartbeat cycle. We design a two-step process to divide and align the heartbeat using the signals in the reference interval as follows. 4.2 Heart Rate Estimation Given a new SCG sequence, the first step is to use a heart rate estimation algorithm, as shown in Figure 6, to measure the heart rates. To estimate the heart rates, we first use a linear interpolation algorithm to normalize the accelerometer readings to a standard sampling rate (e.g., 100 Hz). This step ensures that our system can work on mobile phones that have different sampling rates for the accelerometer. The second step of heart rate estimation is to derive a coarse-template of the reference ATC-RF interval from the SCG signals. To identify the ATC-RF interval, we first locate all the peaks (local maximum points) in an SCG sequence with a two-second duration. We assume that the heart rates of the user are between 50 BPM and 120 BPM. Therefore, there is at least one full heartbeat cycle in the two-second SCG signal. We sort the local maximum points by their amplitudes as the labels shown in Figure 7(a). We then perform a pruning algorithm to remove noisy peaks. Starting from the highest peaks, we add the peaks into a candidate set one-by-one in the descending order of their amplitudes. If the current peak is within a time interval of τ to one of the candidate peaks in the set, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 2, No. 3, Article 140. Publication date: September 2018