In this paper, we propose a people counting algorithm using an impulse radio ultra-wideband radar sensor. The proposed algorithm is based on a strategy of understanding the pattern of the received signal according to the number of people, not detecting each of a large number of people in the radar's received signal. To understand the pattern of the signal, we detect the major clusters from the signal and find the amplitudes of main pulses having the maximum amplitude among the pulses constituting each cluster. We generate a probability density function of the amplitudes of the main pulses from the major clusters according to the number of people and distances. Then, we derive maximum likelihood (ML) equation for people counting. Using the derived ML equation, real-time people counting is possible with a small amount of computation. In addition, since the proposed algorithm does not detect individual clusters for each person but based on the overall cluster behavior of the signals according to the number of people, it enables people counting even in a dense multipath environment, such as a metal-rich environment. In order to prove that the proposed algorithm can be operated in real time in various environments, we performed experiments in an indoor environment and an elevator with a metal structure. Experimental results show that people counting is performed with an mean absolute error of less than one person on average.

Movement disorders, such as Parkinson’s disease, dystonia, tic disorder, and attention-deficit/hyperactivity disorder (ADHD) are clinical syndromes with either an excess of movement or a paucity of voluntary and involuntary movements. As the assessment of most movement disorders depends on subjective rating scales and clinical observations, the objective quantification of activity remains a challenging area. The purpose of our study was to verify whether an impulse radio ultra-wideband (IR-UWB) radar sensor technique is useful for an objective measurement of activity. Thus, we proposed an activity measurement algorithm and quantitative activity indicators for clinical assistance, based on IR-UWB radar sensors. The received signals of the sensor are sufficiently sensitive to measure heart rate, and multiple sensors can be used together to track the positions of people. To measure activity using these two features, we divided movement into two categories. For verification, we divided these into several scenarios, depending on the amount of activity, and compared with an actigraphy sensor to confirm the clinical feasibility of the proposed indicators. The experimental environment is similar to the environment of the comprehensive attention test (CAT), but with the inclusion of the IR-UWB radar. The experiment was carried out, according to a predefined scenario. Experiments demonstrate that the proposed indicators can measure movement quantitatively, and can be used as a quantified index to clinically record and compare patient activity. Therefore, this study suggests the possibility of clinical application of radar sensors for standardized diagnosis.