Radar-Based Identification of Individuals Using Heartbeat Features Extracted from Signal Amplitude and Phase

This study proposes a non-contact method for identifying individuals through the use of heartbeat features measured with millimeter-wave radar. Although complex-valued radar signal spectrograms are commonly used for this task, little attention has been paid to the choice of signal components, namely, whether to use amplitude, phase, or the complex signal itself. Although spectrograms can be constructed independently from amplitude or phase information, their respective contributions to identification accuracy remain unclear. To address this issue, we first evaluate identification performance using spectrograms derived separately from amplitude, phase, and complex signals. We then propose a feature fusion method that integrates these three representations to enhance identification accuracy. Experiments conducted with a 79-GHz radar system and involving six participants achieved an identification accuracy of 97.67%, demonstrating the effectiveness of the proposed component-wise analysis and integration approach.

Radar-based Measurement of the Body Movements of Multiple Students in Classroom Environments

We demonstrate the feasibility of the radar-based measurement of body movements in scenarios involving multiple students using a pair of 79-GHz millimeter-wave radar systems with array antennas. We quantify the body motion using the Doppler frequency calculated from radar echoes. The measurement accuracy is evaluated for two experimental scenarios, namely university students in an office and elementary school students in a classroom. The body movements measured using the two radar systems are compared to evaluate the repeatability and angle dependency of the measurement. Moreover, in the first scenario, we compare the radar-estimated body movement with subjective evaluation scores provided by two evaluators. In the first scenario, the coefficient of correlation between the radar-estimated body movement and the subjective evaluation score is 0.73 on average, with a maximum value of 0.97; in the second scenario, the average correlation coefficient of body movements measured using two radar systems is as high as 0.78. These results indicate that the proposed approach can be used to monitor the body movements of multiple students in realistic scenarios.

Individual Identification Using Radar-Measured Respiratory and Heartbeat Features

This study proposes a method for radar-based identification of individuals using a combination of their respiratory and heartbeat features. In the proposed method, the target individual's respiratory features are extracted using the modified raised-cosine-waveform model and their heartbeat features are extracted using the mel-frequency cepstral analysis technique. To identify a suitable combination of features and a classifier, we compare the performances of nine methods based on various combinations of three feature vectors with three classifiers. The accuracy of the proposed method in performing individual identification is evaluated using a 79-GHz millimeter-wave radar system with an antenna array in two experimental scenarios and we demonstrate the importance of use of the combination of the respiratory and heartbeat features in achieving accurate identification of individuals. The proposed method achieves accuracy of 96.33% when applied to a five-day dataset of six participants and 99.39% when applied to a public one-day dataset of thirty participants.