Hand Gesture Recognition Using a Radar Echo I–Q Plot and a Convolutional Neural Network

We propose a hand gesture recognition technique using a convolutional neural network applied to radar echo inphase/quadrature (I/Q) plot trajectories. The proposed technique is demonstrated to accurately recognize six types of hand gestures for ten participants. The system consists of a low-cost 2.4-GHz continuous-wave monostatic radar with a single antenna. The radar echo trajectories are converted to low-resolution images and are used for the training and evaluation of the proposed technique. Results indicate that the proposed technique can recognize hand gestures with average accuracy exceeding 90%

Laser-Based Noncontact Blood Pressure Estimation Using Human Body Displacement Waveforms

2022 IEEE/MTT-S International Microwave Symposium - IMS 2022, 19-24 June 2022, Denver, CO, USAMeasurement of the body's displacement at multiple positions allows heart pulse wave propagation to be observed; this is an important step toward noncontact blood pressure measurement. This study investigates the feasibility of performing blood pressure measurements using skin displacement waveforms measured at two positions on a human body. To evaluate the accuracy of the proposed approach, this study uses a pair of laser displacement sensors to enable precise pulse transit time measurement. By comparing the displacement waveforms from the two sensors, the relationship between pulse transit time and blood pressure was evaluated. It is demonstrated experimentally that the blood pressure can be estimated with accuracy of 5.1 mmHg, which is equivalent to the error of an ordinary cuff-type blood pressure monitor

Blind Separation of Human Heartbeats and Breathing by the use of a Doppler Radar Remote Sensing

International audienc

Source separation applied to heartbeat doppler radar

International audienc

Comparison of heart rate estimators for doppler radar monitoring

International audienc

Physiological Motion Sensing via Channel State Information in NextG Millimeter-Wave Communications Systems

Wireless communications systems provide channel state information (CSI), which can be used to characterize the physical propagation environment. The small physiological motion of human subjects in that environment, such as that associated with respiration, can modulate the CSI, thus allowing wireless physiological sensing through which a communications system detects and monitors cardiopulmonary motion. NextG millimeter-wave communications systems present even greater opportunities for wireless physiological sensing due to the advantages of small wavelength and high directionality. But challenges also arise due to the multipath effect and phase aliasing caused by larger-than-wavelength motion displacement. This work introduces a comprehensive mathematical CSI model to accurately characterize physiological motion captured by the amplitude and phase of the CSI of a millimeter-wave communications system. The model allows for the interpretation of intricate CSI pattern variations to avoid aliasing error and has been validated with experiments involving both parametric measures conducted with a robotic mover and respiration rate measurements for human subjects. In all cases, rate measurements could be consistently resolved within 10%, or 0.02 Hz, demonstrating the potential for incorporating physiological sensing in NextG millimeter-wave communications systems