## 5 Conclusions  
Two algorithms were proposed and evaluated to detect the peak of chest vibration measured by an accelerometer without ECG signals for real-time application. Both algorithms are based on DWT using either biorthogonal 3.9 or reverse biorthogonal 3.9 mother wavelets. Their performance was evaluated by peak detection error rate, defined as the ratio of missing and additional peaks to the ground truth. In addition to performance in resting state, they were evaluated against subject age and various breathing tasks. Both algorithms showed good performance for signals recorded in the resting state. Especially for the MRI compatible accelerometer, the algorithms are of high interest since during typical MRI measurement patients are in a supine position and usually resting. However, the breathing tasks challenged both, lowering their performances. Of the two, _Slice and Detect_ looks more promising for further exploration. Because the accelerometers used in this study are meant for different patient monitoring applications and were used in different protocols, a meaningful comparison between their performance is not reasonable.  
Determining decomposition level based on the correlation coefficient between the reconstructed and the original signal seems to provide a good solution, as opposed to using the same level for all. It gives the algorithms better ability to handle variations in the signal.  
Both algorithms worked well in the resting state but struggled in the various breathing tasks. However, _Slice and Detect_ looked promising for further investigation as DWT may provide a good separation between adjacent chest vibrations. Nevertheless, they ran into difficulties in the Valsalsva manoeuvre, when the heartbeat raised and dropped quickly. Increasing the efficiency of the algorithms to handle various breathing tasks will be part of future work. This algorithm can be used to assess HR from SCG. Another application includes calculating pulse transit time based on another biosignal to study various cardiovascular parameters and cerebral autoregulation and circulation [23].  
Future works include comparing the performance of both algorithms on different types of accelerometers and investigating dual tree complex wavelet for wavelet decomposition. To do it properly, signals must be measured simultaneously using the selected accelerometers. Other interesting investigations are studying the damping factor of soft materials placed under the accelerometer and employing dual tree complex wavelet analysis.  
Conceptualization all authors; data curation H.F. and E.S.; methodology H.F.; software H.F.; validation H.F. and E.S.; writing--original draft preparation H.F.; writing--review and editing all authors; visualization, H.F. and E.S.; funding acquisition and organizing the data collection T.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Academy of Finland, grant number 318347 and 335723.  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Oulu University Hospital (approval number 296/2018 for children/infants and 53/2012 for adults).  
Informed Consent Statement: Informed consent was obtained from all subjects and parents of the infants/children involved in the study.  
The authors declare no conflict of interest.