Machine learning-empowered heart rate sensors show progress in data collection and processing, though current solutions cannot yet operate independently in a diagnostic context.
Systematic Review
Machine learning applications in heart rate sensors are progressing but currently require further development before independent diagnostic use.
The rapid advances in science and technology in the field of artificial neural networks have led to noticeable interest in the application of this technology in medicine. Given the need to develop medical sensors that monitor vital signs to meet both people's needs in real life and in clinical research, the use of computer-based techniques should be considered. This paper describes the latest progress in heart rate sensors empowered by machine learning methods. The paper is based on a review of the literature and patents from recent years, and is reported according to the PRISMA 2020 statement. The most important challenges and prospects in this field are presented. Key applications of machine learning are discussed in medical sensors used for medical diagnostics in the area of data collection, processing, and interpretation of results. Although current solutions are not yet able to operate independently, especially in the diagnostic context, it is likely that medical sensors will be further developed using advanced artificial intelligence methods.
Staszak et al. (Sun,) conducted a systematic review in Heart health monitoring. Machine learning methods in heart rate sensors was evaluated. Machine learning-empowered heart rate sensors show progress in data collection and processing, though current solutions cannot yet operate independently in a diagnostic context.