Bi-LSTM network for multimodal continuous human activity recognition and fall detection

This paper presents a framework based on multi-layer bi-LSTM network (bidirectional Long Short-Term Memory) for multimodal sensor fusion to sense and classify daily activities’ patterns and high-risk events such as falls. The data collected in this work are continuous activity streams from FMCW radar and three wearable inertial sensors on the wrist, waist, and ankle. Each activity has a variable duration in the data stream so that the transitions between activities can happen at random times within the stream, without resorting to conventional fixed-duration snapshots. The proposed bi-LSTM implements soft feature fusion between wearable sensors and radar data, as well as two robust hard-fusion methods using the confusion matrices of both sensors. A novel hybrid fusion scheme is then proposed to combine soft and hard fusion to push the classification performances to approximately 96% accuracy in identifying continuous activities and fall events. These fusion schemes implemented with the proposed bi-LSTM network are compared with conventional sliding window approach, and all are validated with realistic “leaving one participant out” (L1PO) method (i.e. testing subjects unknown to the classifier). The developed hybrid-fusion approach is capable of stabilizing the classification performance among different participants in terms of reducing accuracy variance of up to 18.1% and increasing minimum, worst-case accuracy up to 16.2%

Continuous human motion recognition with a dynamic range-Doppler trajectory method based on FMCW radar

Radar-based human motion recognition is crucial for many applications, such as surveillance, search and rescue operations, smart homes, and assisted living. Continuous human motion recognition in real-living environment is necessary for practical deployment, i.e., classification of a sequence of activities transitioning one into another, rather than individual activities. In this paper, a novel dynamic range-Doppler trajectory (DRDT) method based on the frequency-modulated continuous-wave (FMCW) radar system is proposed to recognize continuous human motions with various conditions emulating real-living environment. This method can separate continuous motions and process them as single events. First, range-Doppler frames consisting of a series of range-Doppler maps are obtained from the backscattered signals. Next, the DRDT is extracted from these frames to monitor human motions in time, range, and Doppler domains in real time. Then, a peak search method is applied to locate and separate each human motion from the DRDT map. Finally, range, Doppler, radar cross section (RCS), and dispersion features are extracted and combined in a multidomain fusion approach as inputs to a machine learning classifier. This achieves accurate and robust recognition even in various conditions of distance, view angle, direction, and individual diversity. Extensive experiments have been conducted to show its feasibility and superiority by obtaining an average accuracy of 91.9% on continuous classification

Wide Band Embedded Slot Antennas for Biomedical, Harsh Environment, and Rescue Applications

For many designers, embedded antenna design is a very challenging task when designing embedded systems. Designing Antennas to given set of specifications is typically tailored to efficiently radiate the energy to free space with a certain radiation pattern and operating frequency range, but its design becomes even harder when embedded in multi-layer environment, being conformal to a surface, or matched to a wide range of loads (environments). In an effort to clarify the design process, we took a closer look at the key considerations for designing an embedded antenna. The design could be geared towards wireless/mobile platforms, wearable antennas, or body area network. Our group at UT has been involved in developing portable and embedded systems for multi-band operation for cell phones or laptops. The design of these antennas addressed single band/narrowband to multiband/wideband operation and provided over 7 bands within the cellular bands (850 MHz to 2 GHz). Typically the challenge is: many applications require ultra wide band operation, or operate at low frequency. Low frequency operation is very challenging if size is a constraint, and there is a need for demonstrating positive antenna gain

Doppler radar-based non-contact health monitoring for obstructive sleep apnea diagnosis: A comprehensive review

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage

A Compact Ultra Wide-Band Radar System for See-Through-Wall Applications

A compact Ultra wide-band (UWB) radar system for through-wall applications has been developed. Lightweight, portable and low in power consumption, it is configurable for both bistatic and monostatic operation. It uses low cost, off-the-shelf surface mount components, and is ideally suited for ranging, 3d-imaging, and wall characterization. Tests show excellent pulse width generation, resulting in very broadband transmission (0.7 – 5.6 GHz) and good receiver dynamic range, resulting in accurate measurement capabilities

Improved Subset Generation For The MU-Decoder

The MU-Decoder is a hardware subset generator that finds use in partial reconfiguration of FPGAs and in numerous other applications. It is capable of generating a set S of subsets of a large set Z_n with n elements. If the subsets in S satisfy the “isomorphic totally- ordered property”, then the MU-Decoder works very efficiently to produce a set of u subsets in O(log n) time and Θ(n √u log n) gate cost. In contrast, a vain approach requires Θ(un) gate cost. We show that this low cost for the MU-Decoder can be achieved without the isomorphism constraint, thereby allowing S to include a much wider range of subsets. We also show that if additional constraints on the relative sizes of the subsets in S can be placed, then u subsets can be generated with Θ(n √u) cost. This uses a new hardware enhancement proposed in this thesis. Finally, we show that by properly selecting S and by using some elements of traditional methods, a set of Θ (un^log( log (n/log n))) subsets can be produced with Θ(n √u) cost

Design and application of microstrip leaky wave antennas for radar sensing

textThis dissertation investigates the application of the frequency-scanned beam of a microstrip leaky wave antenna (LWA) to track humans in the two-dimensional (2-D) range-azimuth plane. The history, operating principles and frequency-scanned properties of a microstrip LWA are first reviewed. The basic concept of using a microstrip LWA to track humans is verified by designing, building and testing a broadband microstrip LWA, developing the necessary processing algorithm, and collecting data using a vector network analyzer. A number of topics are then investigated to further advance the concept. First, the idea of combining the frequency-scanned antenna with a short-pulse ultra-wideband (UWB) radar is developed to realize a portable, real-time system for human tracking. The radar concept and the components of the system are discussed in detail. Line-of-sight and through-wall measurements of a human subject are carried out to demonstrate the performance. Second, a new LWA structure is proposed to achieve a narrower azimuth beam, which requires both a small leaky-wave attenuation constant and a long aperture. The transverse resonance method (TRM) is applied to analyze the proposed structure and the results are verified with measurements of a built prototype. Third, a new signal processing technique, compressive sensing, is applied to further improve the resolution in both the azimuth and down range dimensions. The technique is tested with simulation and measurement data and is shown to produce sharper target responses in both the down range and azimuth dimensions. Lastly, the radar cross-section (RCS) of a microstrip LWA is studied. The antenna mode scattering and structural mode scattering are modeled separately. A ray picture is provided to explain the observed time-domain features using the group delay of the leaky wave.Electrical and Computer Engineerin

Non-Contact Human Motion Sensing Using Radar Techniques

Human motion analysis has recently gained a lot of interest in the research community due to its widespread applications. A full understanding of normal motion from human limb joint trajectory tracking could be essential to develop and establish a scientific basis for correcting any abnormalities. Technology to analyze human motion has significantly advanced in the last few years. However, there is a need to develop a non-invasive, cost effective gait analysis system that can be functional indoors or outdoors 24/7 without hindering the normal daily activities for the subjects being monitored or invading their privacy. Out of the various methods for human gait analysis, radar technique is a non-invasive method, and can be carried out remotely. For one subject monitoring, single tone radars can be utilized for motion capturing of a single target, while ultra-wideband radars can be used for multi-subject tracking. But there are still some challenges that need to be overcome for utilizing radars for motion analysis, such as sophisticated signal processing requirements, sensitivity to noise, and hardware imperfections. The goal of this research is to overcome these challenges and realize a non-contact gait analysis system capable of extracting different organ trajectories (like the torso, hands and legs) from a complex human motion such as walking. The implemented system can be hugely beneficial for applications such as treating patients with joint problems, athlete performance analysis, motion classification, and so on