Abstract
Continuous and unobtrusive monitoring of daily human activities in homes can potentially improve the quality of life and prolong independent living for the elderly and people with chronic diseases by recognizing normal daily activities and detecting gradual changes in their conditions. However, existing human activity recognition (HAR) solutions employ wearable and video-based sensors, which either require dedicated devices to be carried by the user or raise privacy concerns. Radar sensors enable non-intrusive long-term monitoring, while they can exploit existing communication systems, e.g., Wi-Fi, as illuminators of opportunity. This survey provides an overview of passive radar system architectures, signal processing techniques, feature extraction, and machine learning's role in HAR applications. Moreover, it points out challenges in wireless human activity sensing research like robustness, privacy, and multiple user activity sensing and suggests possible future directions, including the coexistence of sensing and communications and the construction of open datasets.