A fast living-body localization algorithm using timedifferential channel suitable for multi-path environments is introduced. In this method, a time-differential channel is calculated from the difference among the observed channels that correspond to known biological activities such as respiration and heartbeat. The living-body locations are estimated by applying two-dimensional MUltiple SIgnal Classification (MUSIC) to the correlation matrix calculated from the time-differential channels. Experiments were carried out in an indoor environment, and the results show that the proposed localization algorithm could well estimate multiple device-free living-bodies simultaneously. The experimental results demonstrate that the locations of up to three persons could be estimated faster, within a few seconds, and more accurately than the conventional method. One, two or three targets yield 90% values of localization errors of 0.23 m, 0.41 m and 1.29 m, respectively.

The elderly are constantly in danger of falling and injuring themselves without anyone realizing it. A safety-monitoring system based on microwaves can ease these concerns. The authors have proposed safety-monitoring systems that use multiple-input multiple-output (MIMO) radar to localize persons by capturing their biological activities such as respiration. However, our studies to date have focused on localization, which is easier to achieve than an estimation of human postures. This paper proposes a human posture identification scheme based on height and a Doppler radar cross section (RCS) as estimated by a MIMO array. This scheme allows smart home applications to dispense with contact and wearable devices. Experiments demonstrate that this method can identify the supine position (i.e., after a fall) with 100% accuracy, and the average identification rate is 95.0%.

This paper proposes a method of using a microwave MIMO array to recognize different people; it allows smart home applications to dispense with contact and wearable devices. This method captures and processes the time-variant channel generated by a human body present within the field of a MIMO antenna. The time-variant channel is measured, and the spatial and temporal correlation are used for identification by comparing the captured data to reference data. The proposed method identifies the subject as the registrant with maximum correlation. Experiments involving eight known subjects and four imposters demonstrate that the proposed method offers accurate identification with no false positives. The results show that an 8x8 array can achieve the equal error rate (EER) of 0% when the number of identification channels is 5 and above.

The authors have proposed a direction estimation method of the living-body using array antenna in multi-path environment. However, this method has a problem that takes time as several tens of seconds. In this paper, we present a fast algorithm for estimating living-body direction. The experiment showed that the proposed method works well in estimating living-body direction with high accuracy in multi-path environment even when the observation time is less than conventional method.

This letter introduces an antenna array calibration method that uses the channel response with known subject direction so as to accurately estimate living-body directions in multipath environments for elderly monitoring. Said calibration is extracted from the known living-body direction information using the Fourier transformed channel matrix. An experiment shows that the proposed method offers successful antenna array calibration even in indoor environments. Living-body directions are successfully estimated within acceptable error proving that the proposed method is effective in calibrating living-body radars.

This paper presents a method that uses microwaves to estimate human body orientation. The antennas are arranged to surround the human and observe vital signs such as respiration and heart beat from the microwaves reflected from the human. Since the signal reflected from the front of the human will fluctuate the most, mainly due to respiration, human body orientation is estimated by finding the antenna that captures the largest rhythmic fluctuation. In experiments with three subjects, the median value of angular error of human orientation was 9.01∼23.35°.