Non-invasive photoplethysmography (PPG) technology was developed to track heart rate during motion. Automated analysis of PPG has made it useful in both clinical and non-clinical applications. However, PPG-based heart rate tracking is a challenging problem due to motion artifacts (MAs) which are main contributors towards signal degradation as they mask the location of heart rate peak in the spectra. A practical analysis system must have good performance in MA removal as well as in tracking. In this article, we have presented state-of-art techniques in both areas of the automated analysis, i.e., MA removal and heart rate tracking, and have concluded that adaptive filtering and multi-resolution decomposition techniques are better for MA removal and machine learning-based approaches are future perspective of heart rate tracking. Hence, future systems will be composed of machine learning-based trackers fed with either empirically decomposed signal or from output of adaptive filter.

开发了非侵入性光电容积描记术（PPG）技术来跟踪运动期间的心率。PPG的自动分析使其可用于临床和非临床应用。但是，基于PPG的心率跟踪是一个具有挑战性的问题，因为运动伪影（MA）是导致信号衰减的主要因素，因为它们掩盖了频谱中的心率峰值的位置。实用的分析系统必须在去除MA和跟踪方面都具有良好的性能。在本文中，我们介绍了自动分析的两个领域的最新技术，即MA去除和心率跟踪，并得出结论认为自适应滤波和多分辨率分解技术对于MA去除和机器学习更好基于方法的是心率跟踪的未来前景。因此，