# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 6 Conclusions  
In this paper, we thoroughly investigated the sources of power consumption in a modern wireless ECG sensor and proposed analytical power consumption models for the most important activity modes. We designed a model for active ECG streaming mode, which slightly overshoots the measured average current consumption for higher ECG sampling rates, but nevertheless serves as a good predictor of the wireless sensor autonomy.  
The limitation of the presented approach is that for each new platform which we want to estimate power consumption for, we need to make measurements of all the parameters used in the proposed models. This arises from the variability of the platforms available on the market for BLE sensors. Different BLE modules may have different base power consumption due to different implementations of the communication stack. The central processing unit (CPU) used for the application can also have a large impact on the power consumption, depending on the CPU performance and the complexity of the application running on it. The power consumption estimates can be used for similar platforms, but without the measurement of base current consumption events, significant deviations from the results presented in this manuscript are to be expected.  
Based on the presented results, we can conclude that it is not possible to make a power consumption model for the general use of BLE devices. There are some useful guidelines though regarding the manner the current consumption of BLE devices can be tamed. The results show that the transmit power has a negligible effect on the sensor autonomy in the case of streaming data with high sampling rates. The most energy can be saved by lowering the sampling rate and by packing as much data as possible in a single BLE packet. Finally, selecting a suitable connection interval for the applied sampling rate can have the strongest energy-saving effect.  
Further optimizing such a sensor is a difficult task. We propose two scenarios in which the battery could be either increased or decreased according to current usage patterns. Apart from the battery capacity, other sensor hardware and software parameters cannot be optimized for significant power consumption reduction.  
Conceptualization, K.B.; data curation, K.B. and T.K.; formal analysis, K.B.; investigation, K.B., T.K. and M.D.; methodology, K.B.; software, M.D.; validation, M.D. and V.A.; visualization, K.B. and M.D.; writing--original draft, K.B., M.D. and A.R.; writing--review and editing, K.B., M.D., V.A. and A.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Slovenian Research Agency through grant P2-0095 and in part by the European Commission through the H2020 SAAM project under grant agreement No. 769661.  
Not applicable.  
The authors thank [PERSON] for his contributions to the paper revisions by providing constructive feedback on the content and also for his leading role in the wireless ECG sensor development. The authors also thank [PERSON]\(\check{\text{c}}\) for all his contributions to the software and the firmware of the wireless ECG sensor system.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ACI & Application Controller Interface \\ ADC & Analog-to-digital converter \\ A/D & Analog-to-digital \\ BLE & Bluetooth Low Energy \\ \end{tabular}