1,960 research outputs found
Contactless Human Activity Recognition using Deep Learning with Flexible and Scalable Software Define Radio
Ambient computing is gaining popularity as a major technological advancement
for the future. The modern era has witnessed a surge in the advancement in
healthcare systems, with viable radio frequency solutions proposed for remote
and unobtrusive human activity recognition (HAR). Specifically, this study
investigates the use of Wi-Fi channel state information (CSI) as a novel method
of ambient sensing that can be employed as a contactless means of recognizing
human activity in indoor environments. These methods avoid additional costly
hardware required for vision-based systems, which are privacy-intrusive, by
(re)using Wi-Fi CSI for various safety and security applications. During an
experiment utilizing universal software-defined radio (USRP) to collect CSI
samples, it was observed that a subject engaged in six distinct activities,
which included no activity, standing, sitting, and leaning forward, across
different areas of the room. Additionally, more CSI samples were collected when
the subject walked in two different directions. This study presents a Wi-Fi
CSI-based HAR system that assesses and contrasts deep learning approaches,
namely convolutional neural network (CNN), long short-term memory (LSTM), and
hybrid (LSTM+CNN), employed for accurate activity recognition. The experimental
results indicate that LSTM surpasses current models and achieves an average
accuracy of 95.3% in multi-activity classification when compared to CNN and
hybrid techniques. In the future, research needs to study the significance of
resilience in diverse and dynamic environments to identify the activity of
multiple users
Multi-function RF for Situational Awareness
Radio frequency (RF) communications are an integral part of many situational awareness applications. Sensing data need to be processed in a timely manner, making it imperative to have a robust and reliable RF link for information dissemination. Moreover, there is an increasing need for exploiting RF communication signals directly for sensing, leading to the notion of multi-function RF.
In the first part of this dissertation, we investigate the development of a robust Multiple-Input Multiple-Output (MIMO) communication system suitable for airborne platforms.Three majors challenges in realizing MIMO capacity gain in airborne environment are addressed: 1) antenna blockage due largely to the orientation of the antenna array; 2) the presence of unknown interference inherent to the intended application; 3) the lack of channel state information (CSI) at the transmitter. Built on the Diagonal Bell-Labs Layered Space-Time (D-BLAST) MIMO architecture, the system integrates three key design approaches: spatial spreading to counter antenna blockage; temporal spreading to mitigate signal to interference and noise ratio degradation due to intended or unintended interference; and a simple low rate feedback scheme to enable real time adaptation in the absence of full transmitter CSI. Extensive experiment studies using a fully functioning MIMO system validate the developed system.
In the second part, ambient RF signals are exploited to extract situational awareness information directly. Using WiFi signals as an example, we demonstrate that the CSI obtained at the receiver contains rich information about the propagation environment. Two distinct learning systems are developed for occupancy detection using passive WiFi sensing. The first one is based on deep learning where a parallel convolutional neural network (CNN) architecture is designed to extract useful information from both magnitude and phase of the CSI. Pre-processing steps are carefully designed to preserve human motion induced channel variation while insulating against other impairments and post-processing is applied after CNN to infer presence information for instantaneous motion outputs. To alleviate the need of tedious training efforts involved in deep learning based system, a novel learning problem with contaminated sampling is formulated. This leads to a second learning system: a two-stage solution for motion detection using support vector machines (SVM). A one-class SVM model is first evaluated whose training data are from human free environment only. Decontamination of human presence data using the one-class SVM is done prior to motion detection through a two-class support vector classifier. Extensive experiments using commercial off-the-shelf WiFi devices are conducted for both systems. The results demonstrate that the learning based RF sensing provides a viable and promising alternative for occupancy detection as they are much more sensitive to human motion than passive infrared sensors which are widely deployed in commercial and residential buildings
A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications
The commercial availability of low-cost millimeter wave (mmWave)
communication and radar devices is starting to improve the penetration of such
technologies in consumer markets, paving the way for large-scale and dense
deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the
same time, pervasive mmWave access will enable device localization and
device-free sensing with unprecedented accuracy, especially with respect to
sub-6 GHz commercial-grade devices. This paper surveys the state of the art in
device-based localization and device-free sensing using mmWave communication
and radar devices, with a focus on indoor deployments. We first overview key
concepts about mmWave signal propagation and system design. Then, we provide a
detailed account of approaches and algorithms for localization and sensing
enabled by mmWaves. We consider several dimensions in our analysis, including
the main objectives, techniques, and performance of each work, whether each
research reached some degree of implementation, and which hardware platforms
were used for this purpose. We conclude by discussing that better algorithms
for consumer-grade devices, data fusion methods for dense deployments, as well
as an educated application of machine learning methods are promising, relevant
and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys &
Tutorials (IEEE COMST
Contactless Small-Scale Movement Monitoring System Using Software Defined Radio for Early Diagnosis of COVID-19
The exponential growth of the novel coronavirus disease (N-COVID-19) has affected millions of people already and it is obvious that this crisis is global. This situation has enforced scientific researchers to gather their efforts to contain the virus. In this pandemic situation, health monitoring and human movements are getting significant consideration in the field of healthcare and as a result, it has emerged as a key area of interest in recent times. This requires a contactless sensing platform for detection of COVID-19 symptoms along with containment of virus spread by limiting and monitoring human movements. In this paper, a platform is proposed for the detection of COVID-19 symptoms like irregular breathing and coughing in addition to monitoring human movements using Software Defined Radio (SDR) technology. This platform uses Channel Frequency Response (CFR) to record the minute changes in Orthogonal Frequency Division Multiplexing (OFDM) subcarriers due to any human motion over the wireless channel. In this initial research, the capabilities of the platform are analyzed by detecting hand movement, coughing, and breathing. This platform faithfully captures normal, slow, and fast breathing at a rate of 20, 10, and 28 breaths per minute respectively using different methods such as zero-cross detection, peak detection, and Fourier transformation. The results show that all three methods successfully record breathing rate. The proposed platform is portable, flexible, and has multifunctional capabilities. This platform can be exploited for other human body movements and health abnormalities by further classification using artificial intelligence
Edge Artificial Intelligence for Real-Time Target Monitoring
The key enabling technology for the exponentially growing cellular communications sector is location-based services. The need for location-aware services has increased along with the number of wireless and mobile devices. Estimation problems, and particularly parameter estimation, have drawn a lot of interest because of its relevance and engineers' ongoing need for higher performance. As applications expanded, a lot of interest was generated in the accurate assessment of temporal and spatial properties.
In the thesis, two different approaches to subject monitoring are thoroughly addressed. For military applications, medical tracking, industrial workers, and providing location-based services to the mobile user community, which is always growing, this kind of activity is crucial.
In-depth consideration is given to the viability of applying the Angle of Arrival (AoA) and Receiver Signal Strength Indication (RSSI) localization algorithms in real-world situations. We presented two prospective systems, discussed them, and presented specific assessments and tests. These systems were put to the test in diverse contexts (e.g., indoor, outdoor, in water...). The findings showed the localization capability, but because of the low-cost antenna we employed, this method is only practical up to a distance of roughly 150 meters. Consequently, depending on the use-case, this method may or may not be advantageous. An estimation algorithm that enhances the performance of the AoA technique was implemented on an edge device.
Another approach was also considered. Radar sensors have shown to be durable in inclement weather and bad lighting conditions. Frequency Modulated Continuous Wave (FMCW) radars are the most frequently employed among the several sorts of radar technologies for these kinds of applications. Actually, this is because they are low-cost and can simultaneously provide range and Doppler data. In comparison to pulse and Ultra Wide Band (UWB) radar sensors, they also need a lower sample rate and a lower peak to average ratio. The system employs a cutting-edge surveillance method based on widely available FMCW radar technology. The data processing approach is built on an ad hoc-chain of different blocks that transforms data, extract features, and make a classification decision before cancelling clutters and leakage using a frame subtraction technique, applying DL algorithms to Range-Doppler (RD) maps, and adding a peak to cluster assignment step before tracking targets. In conclusion, the FMCW radar and DL technique for the RD maps performed well together for indoor use-cases. The aforementioned tests used an edge device and Infineon Technologies' Position2Go FMCW radar tool-set
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