**9. Conclusions**

This review provides many examples to discuss recent advances in arrhythmia detection using flexible and wearable systems that utilize advanced soft materials, flexible designs, and integrated sensors. We summarize the current bioelectric, optoelectric, mechanoelectric, and ultrasonic sensing methods for monitoring various physiological signals related to arrhythmia. These methods are used to detect and classify arrhythmia accurately. We believe that the future of arrhythmia detection lies in further advancements in flexible wearable sensors and automated classification tools using machine learning algorithms. For applications of portable wearable devices in clinical diagnosis, there are areas to improve in terms of materials and sensor performance, such as sensing materials, sensor-to-skin contact quality, impedance control, power-consumption management, miniaturization, wireless data transmissibility, and detection and classification.

**Author Contributions:** Conceptualization, M.G. and W.-H.Y.; writing—original draft preparation, M.G., N.Z. and W.-H.Y.; writing—review and editing, M.G., N.Z. and W.-H.Y.; supervision, W.-H.Y.; project administration, W.-H.Y.; funding acquisition, W.-H.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** We acknowledge the support from the IEN Center for Human-Centric Interfaces and Engineering at Georgia Tech. This study was partially supported by the Institute of Information & communications Technology, Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (2021-0-01517).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** No new data were created or analyzed in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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