Ashikur Rahman

A Low-IF Tag-Based Motion Compensation Technique for Mobile Doppler Radar Life Signs Monitoring

This paper presents low IF techniques for noninvasive detection of vital signs from a mobile short-range Doppler radar platform. Stationary continuous-wave Doppler radar has been used for displacement measurement and vital signs detection. However, on a mobile platform, measurements become challenging due to motion artifacts induced by the platform. In this work, a complete compensated single transceiver radar system for vital signs detection in the presence of platform movement is demonstrated. In earlier related work, motion of the radar module was determined using cameras installed on site. However, practical mobile monitoring applications would preclude the use of such stationary cameras. In this work, an RF tag and a low IF radar architecture with an adaptive noise cancellation technique is employed to extract desired vital signs motion information even in the presence of large platform motion.

Signal processing techniques for vital sign monitoring using mobile short range doppler radar

This paper investigates mobile noncontact vital sign monitoring device for short range application. The radar module is mounted on a programmable linear stage, and precise stage movements are monitored by an optical tracking system. The motion artifacts due to radar system movements are removed using IIR filter and adaptive noise cancellation techniques. The system is capable of extracting respiration rate even in the presence of radar module motion. In many applications, vital sign measurement from a mobile platform will be very useful, i.e., using unmanned vehicle as a first responder in battlefield including other military and medical applications. Our experiments and theoretical techniques provide a baseline that can be potentially used to measure vital signs from any arbitrarily moving radar system.

Noncontact Doppler radar unique identification system using neural network classifier on life signs

A continuous-wave (CW) Doppler radar-based unique-identification system has been studied. Experiments have been performed using a neural network based classifier to uniquely identify individuals based on the variation in their breathing energy, frequency and patterns captured by the radar. Our work shows the possibility of non-contact unique identification where camera based system is not preferred. It is demonstrated that the system is capable of identifying individuals with more than 90% accuracy. This study also has impact on radar-based breathing pattern classification for health diagnostics.

Barcode based hand gesture classification using AC coupled quadrature Doppler radar

This paper presents a novel method of using AC coupled quadrature Doppler radar for gesture classification. A barcode is generated based on time-domain zero-crossing characteristics of quadrature components in reflected signals of hand gesture. Each motion is repeated within 60 seconds to create a distinguishable pattern on a barcode plot, which can be used for differentiation of motion types and gesture classification.

High dynamic range DC coupled CW Doppler radar for accurate respiration characterization and identification

Accurate radar characterization of respiration can allow sleep diagnostics, and unique identification. A low distortion DC coupled system with high signal to noise ratio is required for such characterization and classification. This is especially critical with small signals as with through wall measurements with poor signal to noise ratio (SNR). This paper proposes a technique to improve signal to noise ratio by DC offset management and using the method of zooming in the fractions of the respiratory cycle waveform. Experimental results show a gain increment of 195 and 42% reduction of error in unique identification by complexity analysis techniques. Unique identification of human subjects behind walls has many potential applications such as, security, health monitoring, IoT applications, and virtual reality, and this technique can also benefit respiratory health diagnostics applications.

RF/wireless indoor activity classification

Abstract Recent developments in noninvasive radar measurement of small-scale motion offer great potential for indoor activity classification in applications such as health diagnostics, health monitoring, surveillance, occupancy sensing, and animal research. Combined with wireless body area networks and indoor localization technology, the measurement and recognition of biological motion patterns will shape the future of home and workplace security, safety, and comfort. This chapter will focus on Doppler radar-based sensing systems for activity monitoring. The organization of the chapter is as follows: background information and recent developments, followed by the theory of Doppler radar sensing in activity classification, experimental results for potential applications, and related challenges.

Single-channel radar fusion for quadrature life-sign Doppler radar

A quadrature Doppler radar has been assembled using two single channel x-band MDU1020 radars. The combined radars can resolve null point distortion in physiological monitoring. This work demonstrates that physical offset between two single-channel radars can achieve proper phase shift for quadrature radar channels. Tests were performed with both mechanical targets and human subject.

Signal conditioning for UAV-radar in vital sign monitoring

A customized signal conditioning circuit has been simulated, fabricated and tested for assisting vital sign monitoring using a short distance Doppler radar installed in an unmanned aerial vehicle (UAV). Vital sign detection using UAV in real-time requires wireless transmission of baseband data to the monitoring station. Methods have been devised to achieve this goal.

Synchrosqueezing an effective method for analyzing Doppler radar physiological signals

Doppler radar can monitor vital sign wirelessly. Respiratory and heart rate have time-varying behavior. Capturing the rate variability provides crucial physiological information. However, the common time-frequency methods fail to detect key information. We investigate Synchrosqueezing method to extract oscillatory components of the signal with time varying spectrum. Simulation and experimental result shows the potential of the proposed method for analyzing signals with complex time-frequency behavior like physiological signals. Respiration and heart signals and their components are extracted with higher resolution and without any pre-filtering and signal conditioning.

See-through-wall life sensing using mobile Doppler radar

A see-through-the-wall (STTW) life sign detection from a mobile platform has been studied. Ultrasonic sensors were used for motion artifact detection. An adaptive algorithm was implemented to validate the techniques for motion compensation when using a mobile life sign radar.