4,915 research outputs found
Toward Deep Learning-Based Human Target Analysis
In this chapter, we describe methods toward deep learning-based human target analysis. Firstly, human target analysis in 2D and 3D domains of radar signal is introduced. Furthermore, range-Doppler surface for human target analysis using ultra-wideband radar is described. The construction of range-Doppler surface involves range-Doppler imaging, adaptive threshold detection, and isosurface extraction. In comparison with micro-Doppler profiles and high-resolution range profiles, range-Doppler surface contains range, Doppler, and time information simultaneously. An ellipsoid-based human motion model is designed for validation. Range-Doppler surfaces simulated for different human activities are demonstrated and discussed. With the rapid emergence of deep learning, the development of radar target recognition has been accelerated. We describe several deep learning algorithms for human target analysis. Finally, a few future research considerations are listed to spark inspiration
Spectrum-Free Estimation of Doppler Velocities Using Ultra-Wideband Radar
A method for estimating Doppler velocities using ultra-wideband radar data is presented. Unlike conventional time-frequency analysis, the proposed method can directly obtain Doppler velocities without searching for peaks in a spectrum. By exploiting closed-form solutions for the Doppler velocities, it avoids the trade-off between time and frequency resolution, thus maintaining high time resolution. Both simulations and measurements are used to evaluate the proposed method versus conventional techniques
Doppler Radar for the Extraction of Biomechanical Parameters in Gait Analysis
The applicability of Doppler radar for gait analysis is investigated by
quantitatively comparing the measured biomechanical parameters to those
obtained using motion capturing and ground reaction forces. Nineteen
individuals walked on a treadmill at two different speeds, where a radar system
was positioned in front of or behind the subject. The right knee angle was
confined by an adjustable orthosis in five different degrees. Eleven gait
parameters are extracted from radar micro-Doppler signatures. Here, new methods
for obtaining the velocities of individual lower limb joints are proposed.
Further, a new method to extract individual leg flight times from radar data is
introduced. Based on radar data, five spatiotemporal parameters related to
rhythm and pace could reliably be extracted. Further, for most of the
considered conditions, three kinematic parameters could accurately be measured.
The radar-based stance and flight time measurements rely on the correct
detection of the time instant of maximal knee velocity during the gait cycle.
This time instant is reliably detected when the radar has a back view, but is
underestimated when the radar is positioned in front of the subject. The
results validate the applicability of Doppler radar to accurately measure a
variety of medically relevant gait parameters. Radar has the potential to
unobtrusively diagnose changes in gait, e.g., to design training in prevention
and rehabilitation. As contact-less and privacy-preserving sensor, radar
presents a viable technology to supplement existing gait analysis tools for
long-term in-home examinations.Comment: 13 pages, 9 figures, 2 tables, accepted for publication in the IEEE
Journal of Biomedical and Health Informatics (J-BHI
Toward Unobtrusive In-home Gait Analysis Based on Radar Micro-Doppler Signatures
Objective: In this paper, we demonstrate the applicability of radar for gait
classification with application to home security, medical diagnosis,
rehabilitation and assisted living. Aiming at identifying changes in gait
patterns based on radar micro-Doppler signatures, this work is concerned with
solving the intra motion category classification problem of gait recognition.
Methods: New gait classification approaches utilizing physical features,
subspace features and sum-of-harmonics modeling are presented and their
performances are evaluated using experimental K-band radar data of four test
subjects. Five different gait classes are considered for each person, including
normal, pathological and assisted walks. Results: The proposed approaches are
shown to outperform existing methods for radar-based gait recognition which
utilize physical features from the cadence-velocity data representation domain
as in this paper. The analyzed gait classes are correctly identified with an
average accuracy of 93.8%, where a classification rate of 98.5% is achieved for
a single gait class. When applied to new data of another individual a
classification accuracy on the order of 80% can be expected. Conclusion: Radar
micro-Doppler signatures and their Fourier transforms are well suited to
capture changes in gait. Five different walking styles are recognized with high
accuracy. Significance: Radar-based sensing of human gait is an emerging
technology with multi-faceted applications in security and health care
industries. We show that radar, as a contact-less sensing technology, can
supplement existing gait diagnostic tools with respect to long-term monitoring
and reproducibility of the examinations.Comment: 11 pages, 6 figure
Channel State Information from pure communication to sense and track human motion: A survey
Human motion detection and activity recognition are becoming vital for the applications in
smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to
track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology
NON-CONTACT TECHNIQUES FOR HUMAN VITAL SIGN DETECTION AND GAIT ANALYSIS
Human vital signs including respiratory rate, heart rate, oxygen saturation, blood pressure, and body temperature are important physiological parameters that are used to track and monitor human health condition. Another important biological parameter of human health is human gait. Human vital sign detection and gait investigations have been attracted many scientists and practitioners in various fields such as sport medicine, geriatric medicine, bio-mechanic and bio-medical engineering and has many biological and medical applications such as diagnosis of health issues and abnormalities, elderly care and health monitoring, athlete performance analysis, and treatment of joint problems. Thoroughly tracking and understanding the normal motion of human limb joints can help to accurately monitor human subjects or patients over time to provide early flags of possible complications in order to aid in a proper diagnosis and development of future comprehensive treatment plans.
With the spread of COVID-19 around the world, it has been getting more important than ever to employ technology that enables us to detect human vital signs in a non-contact way and helps protect both patients and healthcare providers from potentially life-threatening viruses, and have the potential to also provide a convenient way to monitor people health condition, remotely. A popular technique to extract biological parameters from a distance is to use cameras. Radar systems are another attractive solution for non-contact human vital signs monitoring and gait investigation that track and monitor these biological parameters without invading people privacy.
The goal of this research is to develop non-contact methods that is capable of extracting human vital sign parameters and gait features accurately.
To do that, in this work, optical systems including cameras and proper filters have been developed to extract human respiratory rate, heart rate, and oxygen saturation. Feasibility of blood pressure extraction using the developed optical technique has been investigated, too. Moreover, a wideband and low-cost radar system has been implemented to detect single or multiple human subject’s respiration and heart rate in dark or from behind the wall. The performance of the implemented radar system has been enhanced and it has been utilized for non-contact human gait analysis. Along with the hardware, advanced signal processing schemes have been enhanced and applied to the data collected using the aforementioned radar system. The data processing algorithms have been extended for multi-subject scenarios with high accuracy for both human vital sign detection and gait analysis. In addition, different configurations of this and high-performance radar system including mono-static and MIMO have been designed and implemented with great success. Many sets of exhaustive experiments have been conducted using different human subjects and various situations and accurate reference sensors have been used to validate the performance of the developed systems and algorithms
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