651 research outputs found
Radar and RGB-depth sensors for fall detection: a review
This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing
Walking Step Monitoring with a Millimeter-Wave Radar in Real-Life Environment for Disease and Fall Prevention for the Elderly
We studied the use of a millimeter-wave frequency-modulated continuous wave radar for gait analysis in a real-life environment, with a focus on the measurement of the step time. A method was developed for the successful extraction of gait patterns for different test cases. The quantitative investigation carried out in a lab corridor showed the excellent reliability of the proposed method for the step time measurement, with an average accuracy of 96%. In addition, a comparison test between the millimeter-wave radar and a continuous-wave radar working at 2.45 GHz was performed, and the results suggest that the millimeter-wave radar is more capable of capturing instantaneous gait features, which enables the timely detection of small gait changes appearing at the early stage of cognitive disorders
Design and Implementation of a Stepped Frequency Continuous Wave Radar System for Biomedical Applications
There is a need to detect vital signs of human (e.g., the respiration and heart-beat rate) with noncontact method in a number of applications such as search and rescue operation (e.g. earthquakes, fire), health monitoring of the elderly, performance monitoring of athletes Ultra-wideband radar system can be utilized for noncontact vital signs monitoring and tracking of various human activities of more than one subject. Therefore, a stepped-frequency continuous wave radar (SFCW) system with wideband performance is designed and implemented for Vital signs detection and fall events monitoring. The design of the SFCW radar system is firstly developed using off-the-shelf discrete components. Later, the system is implemented using surface mount components to make it portable with low cost. The measurement result is proved to be accurate for both heart rate and respiration rate detection within ±5% when compared with contact measurements. Furthermore, an electromagnetic model has been developed using a multi-layer dielectric model of the human subject to validate the experimental results. The agreement between measured and simulated results is good for distances up to 2 m and at various subjects’ orientations with respect to the radar, even in the presence of more than one subject. The compressive sensing (CS) technique is utilized to reduce the size of the acquired data to levels significantly below the Nyquist threshold. In our demonstration, we use phase information contained in the obtained complex high-resolution range profile (HRRP) to derive the motion characteristics of the human. The obtained data has been successfully utilized for non-contact walk, fall and limping detection and healthcare monitoring. The effectiveness of the proposed method is validated using measured results
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
Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features
Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers.
Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections.
The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach
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
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
Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare
Passive radio frequency (RF) sensing and monitoring of human daily activities
in elderly care homes is an emerging topic. Micro-Doppler radars are an
appealing solution considering their non-intrusiveness, deep penetration, and
high-distance range. Unsupervised activity recognition using Doppler radar data
has not received attention, in spite of its importance in case of unlabelled or
poorly labelled activities in real scenarios. This study proposes two
unsupervised feature extraction methods for the purpose of human activity
monitoring using Doppler-streams. These include a local Discrete Cosine
Transform (DCT)-based feature extraction method and a local entropy-based
feature extraction method. In addition, a novel application of Convolutional
Variational Autoencoder (CVAE) feature extraction is employed for the first
time for Doppler radar data. The three feature extraction architectures are
compared with the previously used Convolutional Autoencoder (CAE) and linear
feature extraction based on Principal Component Analysis (PCA) and 2DPCA.
Unsupervised clustering is performed using K-Means and K-Medoids. The results
show the superiority of DCT-based method, entropy-based method, and CVAE
features compared to CAE, PCA, and 2DPCA, with more than 5\%-20\% average
accuracy. In regards to computation time, the two proposed methods are
noticeably much faster than the existing CVAE. Furthermore, for
high-dimensional data visualisation, three manifold learning techniques are
considered. The methods are compared for the projection of raw data as well as
the encoded CVAE features. All three methods show an improved visualisation
ability when applied to the encoded CVAE features
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