Radar and RGB-Depth Sensors for Fall Detection: A Review

Abstract

Abstract-This paper reviews recent works in the literature on the use of systems based on Radar and RGB-Depth sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users' acceptance and compliance, compared with other sensor technologies such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing.

Remote patient monitoring: a comprehensive study

“…This system effectively reduces false positive rates as it gets the fall confirmed and whether to communicate this fall to the corresponding parties and get help is based on the actual confirmation by the user. Cippitelli et al (2017) discuss contactless fall detection monitoring in their review paper. This paper discusses both radar based and RGB and Depth data based methods for fall detection.…”

Multisensor data fusion for human activities classification and fall detection

“…Doppler vs time patterns of moving targets [8]. Microsoft Kinect sensor estimates the coordinates of joints corresponding to different body parts of the monitored subject and records their temporal evolution frame by frame.…”

Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs

“…Shengheng Liu et al in [10] applied short-time fractional Fourier transform (STFrFT) on the data collected by a C-band frequency-modulated continuous-wave (FMCW) radar, and then detected fall [12]. However, until 2017, just one year prior to when our research was conducted [13], none of them used a higher frequency radar operating in W band, for example in 77GHz or 90GHz, as highlighted in [14]. Also, these radar sensor based researches did not show the ability to detect multiple patients' behavior, simultaneously.…”