**5. Conclusions**

This work focused on the mainstream sensing techniques for HAR tasks, aiming to supply a concrete understanding of the variant sensing principles for younger community researchers. We categorized the human activities into three classes: where, what, and how, for body position-related, body action-related, and body status-related services. This taskoriented categorization aims to supply a basic concept of the objectives of human activity recognition. We also categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others, based on the properties of the sensing medium, aiming to give a better understanding of the sensing technique's physical background. Specific sensing modalities were presented in each category with state-of-the-art publications and a discussion of the modality's advantages and limitations. A summary and an outlook of the sensing techniques were also discussed. We hope this survey can help newcomers have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific applications.

**Author Contributions:** Manuscript writing: S.B., M.L.; project administration: P.L. Formal analysis: B.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the European Union project HUMANE-AI-NET (H2020-ICT-2019-3 #952026).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

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