**7. Conclusions**

This article presented a comprehensive overview of the recent progress in the sensing techniques that have been introduced to human activity recognition and motion analysis. Remarkable technical progresses in new sensing devices and methods, innovative mathematical models for feature extraction and classification, novel networking and computing paradigms, convergence with different subject areas have been identified, which have extended to widespread application fields. To provide a comprehensive understanding of the fundamentals, the skeletal based multi-segment models and kinematic modeling of human body parts were firstly presented. Then, the sensing techniques were summarized and classified into six categories: optical tracking, RF sensors, acoustic sensors, inertial sensors, force sensors, and EMG sensors, followed by in-depth discussions about the pros and cons of the proposed evaluation indexes. Further to the sensing devices, the mathematical methods including feature extraction and classification techniques are discussed as well. According to the state-of-the-art HAR techniques, the technical challenges focused on were found to include the limitation of sensing techniques for convenient uses, dependency on PCs for data processing, difficulties in minor gesture recognition, and specificity in human activity recognition and motion analysis. The solutions for these challenges are considered to be the development trend of future studies. Since human activity recognition and moton analysis is a promising way for efficient interaction between human and information systems, it may play a more important role in future IoT enabled intelligient information systems.

**Author Contributions:** Conceptualization, Z.M. and M.Z.; methodology, M.Z. and H.Z.; formal analysis, Z.M.; investigation, M.Z., C.G., and Q.F.; resources, Z.M.; writing—original draft preparation, M.Z., C.G., and Q.F.; writing—review and editing, Z.M. and H.Z.; visualization, supervision, N.G. and Z.Z.; project administration, Z.M.; funding acquisition, Z.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work presented in this paper is supported by the National Natural Science Foundation of China (NSFC) (51805143), Natural Science Foundation of Hebei province (E2019202131), and the Department of Human Resources and Social Security of Hebei Province (E2019050014 and C20190324).

**Acknowledgments:** The authors would like to thank Nondestructive Detection and Monitoring Technology for High Speed Transportation Facilities, Key Laboratory of Ministry of Industry, and Information Technology for support.

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