In this paper, we compare the performances of impulse radio ultra-wideband (IR-UWB) and frequency modulation continuous wave (FMCW) radars in measuring noncontact vital signs such as respiration rate and heart rate. These two type radars have been widely used in various fields and have shown their applicability to extract vital signs in noncontact ways. IR-UWB radar can extract vital signs using distance information. On the other hand, FMCW radar requires phase information to estimate vital signs, and the result can be enhanced with Multi-input Multi-output (MIMO) antenna topologies. By using commercial radar chipsets, the operation of radars under different conditions and frequency bands will also affect the performance of vital sign detection capabilities. We compared the accuracy and signal-to-noise (SNR) ratios of IR-UWB and FMCW radars in various scenarios, such as distance, orientation, carotid pulse, harmonics, and obstacle penetration. In general, the IR-UWB radars offer a slightly better accuracy and higher SNR in comparison to FMCW radar. However, each radar system has its own unique advantages, with IR-UWB exhibiting fewer harmonics and a higher SNR, while FMCW can combine the results from each channel.

In the past few decades, deep learning algorithms have become more prevalent for signal detection and classification. To design machine learning algorithms, however, an adequate dataset is required. Motivated by the existence of several open-source camera-based hand gesture datasets, this descriptor presents UWB-Gestures, the first public dataset of twelve dynamic hand gestures acquired with ultra-wideband (UWB) impulse radars. The dataset contains a total of 9,600 samples gathered from eight different human volunteers. UWB-Gestures eliminates the need to employ UWB radar hardware to train and test the algorithm. Additionally, the dataset can provide a competitive environment for the research community to compare the accuracy of different hand gesture recognition (HGR) algorithms, enabling the provision of reproducible research results in the field of HGR through UWB radars. Three radars were placed at three different locations to acquire the data, and the respective data were saved independently for flexibility.

Recognizing and tracking the targets located behind walls through impulse radio ultra-wideband (IR-UWB) radar provides a significant advantage, as the characteristics of the IR-UWB radar signal enable it to penetrate obstacles. In this study, we design a through-wall radar system to estimate and track multiple targets behind a wall. The radar signal received through the wall experiences distortion, such as attenuation and delay, and the characteristics of the wall are estimated to compensate the distance error. In addition, unlike general cases, it is difficult to maintain a high detection rate and low false alarm rate in this through-wall radar application due to the attenuation and distortion caused by the wall. In particular, the generally used delay-and-sum algorithm is significantly affected by the motion of targets and distortion caused by the wall, rendering it difficult to obtain a good performance. Thus, we propose a novel method, which calculates the likelihood that a target exists in a certain location through a detection process. Unlike the delay-and-sum algorithm, this method does not use the radar signal directly. Simulations and experiments are conducted in different cases to show the validity of our through-wall radar system. The results obtained by using the proposed algorithm as well as delay-and-sum and trilateration are compared in terms of the detection rate, false alarm rate, and positioning error.

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Gait analysis is one of the most basic methods for assessing a patient's biopsychological status. Doctors can distinguish among people with mental and neurological disorders by monitoring their gait. To perform gait analysis in a more quantitative and accurate way, many studies have used inertial measurement units (IMUs), cameras and ground reaction force platforms. However, conventional gait analysis requires sensors to be attached to the subject's body, and some of them are too expensive to afford. Currently, studies of noncontact gait analysis using radar sensors are being researched. Such studies have successfully measured several gait parameters of the noncontact method but have been unable to distinguish between individual legs. In this study, we proposed a method for noncontact gait analysis on a treadmill that could separate the left and right legs using multi-input and multi-output frequencymodulated continuous-wave (MIMO FMCW) radar. By recognizing two legs in a range-Doppler map and estimating their angles, ranges and velocities, the gait parameters of the individual legs could be identified. We performed experiments with 15 participants in 4 scenarios (walking, running, left leg limping, right leg limping) and compared gait parameters obtained using FMCW radar and IMUs. The gait parameter measurements were validated using the intraclass correlation value, and they showed excellent agreement except for flight time. Moreover, a parameter is suggested that can detect gait asymmetry accurately, and its sensitivity (0.83) and specificity (1.00) were validated. Our future research will analyze not only feet movement but also arm movement so that it can be further applied to the medical field.