Advancements in Radar Point Cloud Generation and Usage in Context of Healthcare and Assisted Living Domain: A Review

Abstract

Point clouds (PCs) are ubiquitous data representation schema in complex tasks related to semantic segmentation and scenes understanding. Contrary to vision-based approaches, radars, being a privacy-preserving sensor, are lately getting huge attention in generating PCs for medical applications since such sensors can be embedded into hospitals and living spaces. This paper summarizes the use of radar-generated PCs in healthcare and assisted living domain. Comparative analysis of radar and other technologies is presented briefly, followed by a detailed note on commercial radars for PC generation. Radar PCs data collection, pre-processing, feature extractions, and features processing are reviewed, and a detailed summary of applications related to healthcare and assisted living is presented. Supporting signal processing and machine learning (ML) approaches are also reviewed. Specifically, the dedicated PC oriented ML algorithms are discussed in details. The discussed applications encompass human activity recognition, posture classification, gait recognition and fall detection. Radar PC data is crucial for certain health monitoring and rehabilitation tasks, such as skeletal-joint and pose estimation; the range, Doppler, and angle information of target independently may fall short in such applications. Finally, the paper concludes with a comprehensive summary of current trends, key takeaways, and future directions. Paper also outlines the future prospect of using generative ML for healthcare applications.