*Editorial* **Editorial for the Special Issue on Physical Diagnosis and Rehabilitation Technologies**

**Tao Liu 1,\* and João Paulo Morais Ferreira 2,\***


Recently, physical diagnosis and human motion analysis have become active research topics in bioelectronics, and they have a broad range of applications, such as pathology detection, rehabilitation, prosthesis design, biometric identification, and humanoid robotic locomotion. Clinical human motion analysis methods aim to provide an objective means of quantifying the severity of pathology. A set of pathology-related human motion disorders have been identified and can be used to support diagnosis and the development of new assistive and rehabilitation technologies. This Special Issue in *Electronics*, titled "Physical Diagnosis and Rehabilitation Technologies", compiles some of the recent research accomplishments in the field of robotics and sensors for human assistance. It consists of 10 papers, which cover rehabilitation robots, human–computer interaction, and sensor and data augmentation, including two review papers. These papers can be categorized into four groups as follows:


**Citation:** Liu, T.; Ferreira, J.P.M. Editorial for the Special Issue on Physical Diagnosis and Rehabilitation Technologies. *Electronics* **2022**, *11*, 2247. https:// doi.org/10.3390/electronics11142247

Received: 13 July 2022 Accepted: 15 July 2022 Published: 18 July 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

was designed for user-friendly wearing, with the advantages of comfort, convenience, portability, and affordability. A novel solid–liquid mixture pressure-sensing module is proposed in [9]. A flexible film with unique liquid-filled structures greatly reduces the pulse measurement error caused by sensor misalignment. The device is expected to provide a new solution for continuous wearable BP monitoring.

(4) Data augmentation: An integrated modeling approach incorporating a war trauma severity scoring algorithm (WTSS) and deep neural networks (DNN) is proposed in [10]. The experimental results verified that the proposed approach surpassed the traditional manual generation methods, achieved a prediction accuracy of 84.43%, and realized large-scale and credible war-trauma data augmentation.

**Funding:** This research was funded by Open Fund of the State Key Laboratory of Fluid Power and Mechatronic Systems: GZKF-202101.

**Acknowledgments:** We would like to thank all the authors for the papers they submitted to this Special Issue. We would also like to acknowledge all the reviewers for their careful and timely reviews to help improve the quality of this Special Issue.

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

## **References**

