# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 7 Discussion and Concluding Remarks
Another group of methods is based on signal subtraction in the frequency domain (spectral subtraction). This approach, however, requires an estimation of heart rate frequency and detects deviations from it. This may pose a problem, since the assumption of a normal heart rate to be 1 Hz may not be generalized over all cases. * **Heart rate estimation. ** The mainstream algorithms for HR estimation are TROIKA and JOSS and others directly derived from these two. The reason is that these algorithms have proven to be fairly accurate and effective. Nonetheless, the implementation of TROIKA and JOSS into a wearable device is a challenging task, since these algorithms rely on heavy mathematical computations. Hence, the processing unit and, again, power consumption of the wearable device, pose challenges. * **Potential for a more complete hemodynamic monitoring. ** Apart from the HR estimation, PPG and EBI techniques have remarkable potential to also provide information on numerous other hemodynamic parameters. Such data, including the hemodynamic feature points, denoting, e.g., left ventricular ejection time (LVET) or pulse transit time (PTT), can be utilized already in performing prognostic and diagnostic tasks. However, for such operations, where the differentiation of the signal is performed, the signal quality is of paramount importance. Indeed, up to four derivatives of the PPG signal [169] and first derivative of EBI signal [170] have been studied in order to evaluate the heart and cardiovascular health. To conclude, the motivation behind the integration of the PPG and EBI technologies into wearable devices is that the status of cardiovascular health can be continuously monitored in a non-invasive manner. Today's modalities of wearable devices, such as wrist-worn devices, bracelets, rings and even tattoos have made this process convenient to the user. This has substantial potential in transforming cardiovascular health monitoring. The development of energy-efficient, accurate, and robust wearable sensors is paramount in addressing the current limitations and enhancing the practical applications of these technologies. Future research should focus on optimizing power consumption without compromising measurement accuracy, improving noise filtering and MA removal techniques, and developing innovative materials that enhance sensor reliability and comfort. Collaborative efforts between material scientists, engineers, and healthcare professionals will be essential in driving the next generation of wearable health monitoring devices, ultimately contributing to improved health outcomes and patient care. Conceptualization, D.L. and R.J.; methodology, D.L. and R.J.; validation, D.L. and R.J. and M.M. ; investigation, D.L. and R.J.; writing--original draft preparation, D.L. and R.J.; writing--review and editing, D.L., R.J., M.M. and L.S. ; visualization, D.L. and R.J.; supervision, L.S. ; project administration, L.S. All authors have read and agreed to the published version of the manuscript. This research has been received funding in part by the European Union through the Project PRAESIIDIUM \"Physics Informed Machine Learning-Based Prediction and Reversion of Impaired Fasting Glucose Management\" (call HORIZON-HLTH-2022-STAYHLTH-02) under Grant 101095672, in part by the European Union, and in part by the Estonian Research Council grant PRG1483. We wish to express our sincere thanks to [PERSON] from Institute of Electronics and Computer Science for his pivotal guidance and expertise during the initial phases of our project. The authors declare no conflicts of interest. The following abbreviations are used in this article: