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Capable of remote presentations of human heart activities, the Doppler cardiogram (DCG) can be detected by an agile Doppler radar sensor. However, as a recently developed technology, its performance needs sustained improvements for the future practical applications. In this work, a continuous-wave, ${V}$ -band Doppler radar sensor was implemented to improve the discrimination of the receiver, for detecting micrometer-level details of heartbeat motions conducted to the chest skin. Besides, new algorithms were derived to achieve high linearity, to demodulate the Doppler phases and retrieve the fine details of DCG waveforms. Experimental measurements to different subjects showed significantly enhanced robustness and accuracy. The obtained results imply a promising potential of DCGs in wide application scenarios such as medical wisdom and personal healthcare. [ABSTRACT FROM AUTHOR]

In the past decades, continuous Doppler radar sensor-based biosignal detections have attracted many research interests. A typical example is the Doppler heartbeat detection. While significant progresses have been achieved, reliable, time-domain accurate demodulation of biosignals in the presence of unavoidable direct current (dc) offsets remains a technical challenge. Aiming to overcome this difficulty, we propose in this article a novel demodulation algorithm that does not need to trace and eliminate dynamic dc offsets based on approximating segmented arcs in a quadrature constellation of sampling data to directional chords. Assisted by the principal component analysis, such chords and their directions can be deterministically determined. Simulations and experimental validations showed full recovery of micrometer-level pendulum movements and strongly noised human heartbeats, verifying the effectiveness and accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

Personal devices such as smartphones and tablets are rapidly becoming personal communication, information, and control centers. Apart from multitouch screens, human gestures are considered as a new interactive human–smart device interface. In this work, we propose a noncontact solution to implement hand gesture recognitions for smart devices. It is based on a continuous wave, time-division-multiplexing (TDM), single-input multiple-output (SIMO) Doppler radar sensor that can be realized by slightly modifying existing RF front ends of smart devices, and a machine-learning algorithm to recognize predefined gestures by classifying deterministic Doppler signals. An experimental setup emulating a smartphone-based radar sensor was implemented, and the experimental results verified the robustness and the accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

Noncontact detection of human vital signs based on miniaturized Doppler radar systems (DRSs) can be widely used in healthcare and biomedical applications. Although significant progresses have been achieved, a reliable wireless vital signs detection in the presence of large-scale random human body movements remains a technical challenge. In this paper, based on a comprehensive use of the high-dynamic-range radar architecture and linearized Doppler phase demodulation algorithms, we further introduce matched filters to retrieve the respiration and heartbeat spectra completely concealed under the wideband noise floor caused by large-scale body movements. Along with an existing 5.8-GHz system, a single-board integrated DRS operating at 24 GHz was designed to verify the effectiveness of the used architecture and algorithms. Experimental results comply with the theoretical expectation. The obtained results imply the potential to implement practical bioradar systems for human noncontact vital signs detections. [ABSTRACT FROM AUTHOR]

This paper presents a printed circuit board realization of a $K$ -band portable frequency-modulated continuous-wave radar transceiver with beamforming array for short-range localization. The transmitter channel of the proposed radar consists of a free-running voltage-controlled oscillator (VCO) and a single patch antenna. A linear frequency-modulated chirp signal is generated by the VCO, which is controlled by an analog “sawtooth” voltage generator. The receiver channel has a four-element linear beamforming array, a six-port circuit, and a baseband circuit. The beam of the array can be continuously steered in a range of ±45° on the $H$ -plane through an array of vector controllers. Each vector controller is capable of simultaneously controlling the phase and the amplitude of the corresponding array element. The design principle of the binary-phase-shift attenuator, the vector controller, and the radar system are discussed. Calibration method of the array is introduced to minimize the errors caused by component variation and fabrication. The radiation patterns of the array with phase-only beam steering and phase-amplitude beamforming are measured and compared, demonstrating the advantage of the beamforming design. System-level experiments showed that the proposed solution is suitable for short-range localization. In addition, experiments with a human subject revealed the capability of the proposed radar system to discriminate a human target from other objects based on the vital-Doppler effect. [ABSTRACT FROM AUTHOR]

This paper presents a portable radar system for short-range localization, inverse synthetic aperture radar imaging, and vital sign tracking. The proposed sensor incorporates frequency-modulated continuous-wave (FMCW) and interferometry (Doppler) modes, which enable this radar system to obtain both absolute range information and tiny vital signs (i.e., respiration and heartbeat) of human targets. These two different operation modes can be switched through an on-board microcontroller. To simplify the system, the proposed radar utilizes the audio card of a laptop to sample the baseband signal. The FMCW mode of the radar uses operational-amplifier-based circuits to generate an analog sawtooth signal and a reference pulse sequence (RPS). The RPS is locked to the sawtooth signal to obtain coherence for the radar system. For the interferometry mode, a low-intermediate-frequency modulation method is implemented to avoid the slow vital signs from being distorted by the high-pass filter of the audio card. Several experiments were carried out to reveal the capability and distinct operational features of the proposed portable hybrid radar. The experiments also showed that the system can easily detect glass, which is usually difficult to identify for optical-based sensors. In addition, 2-D scanning in a complex environment revealed that the proposed radar was able to differentiate human targets from other objects. Moreover, ISAR images were used to isolate moving human targets from surrounding clutter. Finally, the proposed radar also demonstrated its ability to accurately measure vital signs when a human subject sits still. [ABSTRACT FROM PUBLISHER]

Short-range continuous-wave Doppler radar sensors have been mainly used for noncontact detection of various motions. In this paper, we investigate the feasibility to implement the function of a remote mouse, an input device of a computer, by recognizing human gestures based on a dual-channel Doppler radar sensor. Direct conversion architecture, symmetric subcarrier modulation, and bandpass sampling techniques are used to obtain a cost-effective solution. An arcsine algorithm and a motion imaging algorithm are proposed to linearly reconstruct the hand and finger motions in a 2-D plane from the demodulated Doppler phase shifts. Experimental results verified the effectiveness of the proposed architecture and algorithms. Different from the frequency-domain “micro-Doppler” approach, the proposed remote gesture recognition based on linear motion reconstruction is able to recognize definitive signatures for the corresponding motions, exhibiting promising potential in practical applications of human–computer interaction. [ABSTRACT FROM PUBLISHER]

Recently, progresses have been made in hand gesture recognition based on Doppler radar sensors. However, it remains a technical challenge to avoid the interfering human motions. An example is detecting hand gestures in the presence of random body movements. In this paper, we propose a solution based on a Single-input Multiple-output frontend and a blind motion separation algorithm. Assisted by an additional receiving channel, Doppler signals caused by different motions can be separated by extending the algorithm originally developed for separating human voices. The experimental separation of hand gestures from interfering movements verified the effectiveness of this approach. The proposed solution can be potentially used in novel applications such as human gait and gesture recognitions. [ABSTRACT FROM AUTHOR]