###### Abstract  
The importance of radar-based human activity recognition has increased significantly over the last two decades in safety and smart surveillance applications due to its superiority in vision-based sensing in the presence of poor environmental conditions like low illumination, increased radiative heat, occlusion, and fog. Increased public sensitivity to privacy protection and the progress of cost-effective manufacturing have led to higher acceptance and distribution of this technology. Deep learning approaches have proven that manual feature extraction that relies heavily on process knowledge can be avoided due to its hierarchical, non-descriptive nature. On the other hand, ML techniques based on manual feature extraction provide a robust, yet empirical-based approach, where the computational effort is comparatively low. This review outlines the basics of classical ML- and DL-based human activity recognition and its advances, taking the recent progress in both categories into account. For every category, state-of-the-art methods are introduced, briefly explained, and their related works summarized. A comparative study is performed to evaluate the performance and computational effort based on a benchmarking dataset to provide a common basis for the assessment of the techniques' degrees of suitability. deep learning; human activity recognition; micro-Doppler; machine learning; radar +
Footnote †: journal: Medical Intelligence and Smart Automation  
intelligent control of heating [20] or the surveillance of elderly people in order to detect falls [9], have led to an unprecedented technological pace. Although the advantages of vision-based sensing and recognition technologies are undisputed, there are many situations where the drawbacks are severe compared to radar-based technologies. Sensing-related problems include lighting conditions (poor illumination), thermal conditions (increased radiative heat), occlusion, and atmospheric phenomena (fog, mirages). Besides these, radar-based systems are independent of privacy-related conditions since the target information does not rely explicitly on target shapes but can be derived from microscale movements based on micro-Doppler signatures [33; 34; 35; 36; 37; 38]. The radar-based recognition of human activities has been studied by numerous authors, where classical machine learning (ML)-based techniques, e.g., k-Means [39] and SVM [40; 41; 42; 43; 44], as well as deep learning-based (DL) approaches, have been used [45; 46; 47; 48; 49; 50]. In general, ML-based techniques rely on shallow heuristically determined features that are characterized by simple statistical properties and thus depend on technological experience. Furthermore, the learning process is restricted to static data and does not take long-term changes in the process data into consideration. Deep learning constitutes a subdomain of machine learning, where the method's applicability does not depend on the suitability of hand-crafted features. Feature extraction is highly reliant on domain knowledge and the expertise of the specific user. Instead, deep learning approaches are able to extract high-level, yet not fully interpretable, information in a generalized approach, and due to their structure, the underlying learning process can be designed to increase computational efficiency, e.g., through parallelization. This work addresses the recent progress in ML-based HAR methods in radar technology settings and focuses on DL-based approaches since these have proven to be more generalized, long-term, and robust solutions for classification problems. One major contribution of this paper is to provide the first comparative study of HAR methods using a common database and a unified approach for the application of the most common DL methods while focusing on key aspects: CNN-, RNN-, and CAE-based methods.