AbstractAnthropometric profiles are important indices for assessing medical conditions, including malnutrition, obesity, and growth disorders. Noncontact methods for estimating those parameters could have considerable value in many practical situations, such as the assessment of young, uncooperative infants or children and the prevention of infectious disease transmission. The purpose of this study was to investigate the feasibility of obtaining noncontact anthropometric measurements using the impulse-radio ultrawideband (IR-UWB) radar sensor technique. A total of 45 healthy adults were enrolled, and a convolutional neural network (CNN) algorithm was implemented to analyze data extracted from IR-UWB radar. The differences (root-mean-square error, RMSE) between values from the radar and bioelectrical impedance analysis (BIA) as a reference in the measurement of height, weight, and body mass index (BMI) were 2.78, 5.31, and 2.25, respectively; predicted data from the radar highly agreed with those from the BIA. The intraclass correlation coefficients (ICCs) were 0.93, 0.94, and 0.83. In conclusion, IR-UWB radar can provide accurate estimates of anthropometric parameters in a noncontact manner; this study is the first to support the radar sensor as an applicable method in clinical situations.