## 4 Discussions  
[PERSON] et al. acknowledged that SCG signal suffers from its complexity [15]. Cardiac beat induces vibration to the skin and the accelerometer measures not only the pure skin vibration, but also its harmonics. The harmonic signals depend on the hardness of the tissue under the sensor. Hard tissue induces a lower damping factor than soft tissue. In the absence of this damping factor, vibrations can be very noisy. However, if the damping factor is too high, the amplitudes of S1\({}^{\prime}\) and S2\({}^{\prime}\) are not clearly visible. As a countermeasure, a soft material can be inserted between the sensor and skin, providing a mechanical coupling [30]. Exploring these effects falls outside of the scope of this study, so future work will be needed to evaluate the proposed algorithms in this respect. We also emphasize that accelerometer placement matters, as confirmed by [PERSON] and [PERSON] [5] and [PERSON] et al. [7]. We placed the accelerometer on the chest, typically above the sternum. However, in the case of Group #1, the sensor was attached after the subjects fell asleep, and some sleeping body positions made placement in the planned position impossible. In Group #2, the accelerometer could be placed on the target area, but, as the subjects were all adults, signal quality was affected by differences in chest area, caused by varying muscle thicknesses and fat deposition, for example. Another problem was the accelerometer cable, especially in Group #1. Children moved freely in their sleep and cable movements or bending sometimes affected signal quality. Many researchers have presented useful algorithms for detecting peaks from the systolic and diastolic phases. The proposed algorithms rely on two assumptions based on PCG, (1) the peak of S1 is always larger than that of S2, and (2) there is an appropriate distance between S2 of the current cardiac cycle and S1 of the next one. However, the first assumption did not always hold for SCG, as shown by the results from Group #1. The main drawback is these solutions often include ECG [10; 11; 12], which is not always available. With the absence of an ECG signal to guide the determination of real peaks, performance evaluation in Group #1 was quite challenging. With three out of seven subjects in Group #1, the amplitude of S2\({}^{\prime}\) was not always smaller than that of S1\({}^{\prime}\). Consequently, the peak position could not be determined. Moreover, children and babies have a higher HR than adults, making the distance between S2\({}^{\prime}\) of the current cardiac cycle and S1\({}^{\prime}\) of the next one even smaller. For this reason, no results were available for data analysis from three of the subjects in Group #1. The two assumptions discussed above were utilised in the algorithms presented here. _Slice and Detect_ uses them to determine the start of each cardiac cycle, based on the peak of the lower envelope of Approximation signals. With an absolute AC larger than the absolute AO, the shape of the envelope changed accordingly. Consequently, the algorithm missed the true starting point of the cardiac cycle. Further, this error propagated to the peak detection results. The other algorithm used the assumptions to find prominent peaks in the envelope of signal reconstruction that leads to AO. Both algorithms work well when the absolute AC is less than the absolute AO within the same cardiac beat. Another important condition is that S1\({}^{\prime}\) and S2\({}^{\prime}\) must be sufficiently sharp. If these conditions are not fulfilled, the envelope and peak detection fail to produce optimal results. In a resting state, both algorithms provided a low error rate for both groups. In this state, disturbance was almost non-existent, except for children and babies moving in their sleep. As a result, signal quality was good, and the low error rate makes sense. For errors produced by the algorithms, additional peak error was always lower than missing peak error, except for Breath Hold and Valsalva for _Detect_ in Group #2.