## 4 Discussions
Both tasks force the subjects to compress their lungs, which may affect chest vibration characteristics. Interestingly, _Slice and Detect_ showed a very low error rate in this situation. Perhaps DWT provided sufficient signal enhancement to provide a good separation between adjacent chest vibrations. _Detect_, on the other hand, enabled heartbeat detection from a very noisy signal (see Figure 9). In a resting state, _Detect_ delivered fewer additional peaks in both groups. The most probable reason is that, in this state, signals were not disturbed by the various breathing tasks the subjects undertook. These tasks affect chest movement, inducing motion artefacts. As hypothesized above, DWT may provide a form of signal enhancement that offers a good separation between adjacent chest vibrations. For Group #1, _Detect_ outperformed the other one in terms of average error rate detection. However, with only four subjects, it is difficult to draw meaningful conclusions from this table. That said, _Detect_ showed promise, especially for monitoring children and babies. During the Valsalva manoeuvre, both algorithms struggled to detect correct peak locations, when the heartbeat increased or dropped quickly. SCG signals were quite noisy during this task, mostly due to body movements. It would be impossible even for a human to pinpoint AO correctly without recourse to R-peaks. Wavelet transform decomposes signals based on chosen mother wavelets and can be used to find parts of a signal that are similar in shape to the mother wavelet. Hence, it is a means of signal enhancement that can find buried information in a noisy signal. However, this ability failed to produce good results in Group #2 during breathing tasks. Using an envelope to find peak candidates in _Detect_ came with a specific drawback: at the beginning of some segments, it failed to detect a prominent peak. If signal quality is good and the measurement is sufficiently long to produce signals with many prominent peaks, this problem can be neglected. However, for a short-time signal with poor quality, this is an important issue. Therefore, methods without envelope detection should be used or a robust parameter should be provided for peak detection, which is a subject for future work. Choosing a decomposition level based on the correlation between the last detail reconstruction and the original signal proved successful. The nature of signal oscillation cannot be generalised, so each segment may require a different decomposition level. Our experiments showed that this level varies from five to eight. Performance measurements in the adult dataset were easier, due to the availability of ECG, especially for signals with artefacts. The correct peak location always comes slightly after the R-peak of each heartbeat. Researchers define this time difference as pulse transit time (PTT), and it can be used, for example, for continuous estimation of blood pressure fluctuations [8] and the study of cerebral autoregulation [31]. Figure 9: _Detect_ uses wavelet transform to decompose signals based on the similarity of their shape with the mother wavelet. It offers the possibility to detect peaks, shown as asterisks that are hard to find even for a human. Our data sets used two different accelerometers, ordinary MEMS for children/infants and opto-mechanical accelerometers for adults. Comparing their performances is only possible from the resting state of these two groups. However, nothing can be concluded thus far, because signals from adults are more controllable than those from children/infants. Consequently, another study must be conducted to make a more meaningful conclusion.