Multi-User Gesture Recognition with Radar Technology

The aim of this work is the development of a Radar system for consumer applications. It is capable of tracking multiple people in a room and offers a touchless human-machine interface for purposes that range from entertainment to hygiene

Fusion of wearable and contactless sensors for intelligent gesture recognition

This paper presents a novel approach of fusing datasets from multiple sensors using a hierarchical support vector machine algorithm. The validation of this method was experimentally carried out using an intelligent learning system that combines two different data sources. The sensors are based on a contactless sensor, which is a radar that detects the movements of the hands and fingers, as well as a wearable sensor, which is a flexible pressure sensor array that measures pressure distribution around the wrist. A hierarchical support vector machine architecture has been developed to effectively fuse different data types in terms of sampling rate, data format and gesture information from the pressure sensors and radar. In this respect, the proposed method was compared with the classification results from each of the two sensors independently. Datasets from 15 different participants were collected and analyzed in this work. The results show that the radar on its own provides a mean classification accuracy of 76.7%, while the pressure sensors provide an accuracy of 69.0%. However, enhancing the pressure sensors’ output results with radar using the proposed hierarchical support vector machine algorithm improves the classification accuracy to 92.5%

Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems

Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, industrial packaging, medical imaging, and non-destructive testing. Traditional methods for perception and imaging are challenged by novel data-driven algorithms that offer improved resolution, localization, and detection rates. Over the past decade, deep learning technology has garnered substantial popularity, particularly in perception and computer vision applications. Whereas conventional signal processing techniques are more easily generalized to various applications, hybrid approaches where signal processing and learning-based algorithms are interleaved pose a promising compromise between performance and generalizability. Furthermore, such hybrid algorithms improve model training by leveraging the known characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep learning algorithms and offering higher performance than conventional methods. This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging systems applicable to a host of problems in perception and sensing. Various problem spaces are explored, including static and dynamic gesture classification; precise hand localization for human computer interaction; high-resolution near-field mmWave imaging using forward synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image super-resolution using deep neural network (DNN) and Vision Transformer (ViT) architectures; and data-level multiband radar fusion using a novel hybrid-learning architecture. Furthermore, we introduce several novel approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen

Multi-User Gesture Recognition with Radar Technology

The aim of this work is the development of a Radar system for consumer applications. It is capable of tracking multiple people in a room and offers a touchless human-machine interface for purposes that range from entertainment to hygiene

Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool

Accelerated by the increasing attention drawn by 5G, 6G, and Internet of Things applications, communication and sensing technologies have rapidly evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years. Enabled by significant advancements in electromagnetic (EM) hardware, mmWave and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz, respectively, can be employed for a host of applications. The main feature of THz systems is high-bandwidth transmission, enabling ultra-high-resolution imaging and high-throughput communications; however, challenges in both the hardware and algorithmic arenas remain for the ubiquitous adoption of THz technology. Spectra comprising mmWave and THz frequencies are well-suited for synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide spectrum of tasks like material characterization and nondestructive testing (NDT). This article provides a tutorial review of systems and algorithms for THz SAR in the near-field with an emphasis on emerging algorithms that combine signal processing and machine learning techniques. As part of this study, an overview of classical and data-driven THz SAR algorithms is provided, focusing on object detection for security applications and SAR image super-resolution. We also discuss relevant issues, challenges, and future research directions for emerging algorithms and THz SAR, including standardization of system and algorithm benchmarking, adoption of state-of-the-art deep learning techniques, signal processing-optimized machine learning, and hybrid data-driven signal processing algorithms...Comment: Submitted to Proceedings of IEE

Positioning and Sensing System Based on Impulse Radio Ultra-Wideband Technology

Impulse Radio Ultra-Wideband (IR-UWB) is a wireless carrier communication technology using nanosecond non-sinusoidal narrow pulses to transmit data. Therefore, the IR-UWB signal has a high resolution in the time domain and is suitable for high-precision positioning or sensing systems in IIoT scenarios. This thesis designs and implements a high-precision positioning system and a contactless sensing system based on the high temporal resolution characteristics of IR-UWB technology. The feasibility of the two applications in the IIoT is evaluated, which provides a reference for human-machine-thing positioning and human-machine interaction sensing technology in large smart factories. By analyzing the commonly used positioning algorithms in IR-UWB systems, this thesis designs an IRUWB relative positioning system based on the time of flight algorithm. The system uses the IR-UWB transceiver modules to obtain the distance data and calculates the relative position between the two individuals through the proposed relative positioning algorithm. An improved algorithm is proposed to simplify the system hardware, reducing the three serial port modules used in the positioning system to one. Based on the time of flight algorithm, this thesis also implements a contactless gesture sensing system with IR-UWB. The IR-UWB signal is sparsified by downsampling, and then the feature information of the signal is obtained by level-crossing sampling. Finally, a spiking neural network is used as the recognition algorithm to classify hand gestures

Automotive gestures recognition based on capacitive sensing

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e ComputadoresDriven by technological advancements, vehicles have steadily increased in sophistication, specially in the way drivers and passengers interact with their vehicles. For example, the BMW 7 series driver-controlled systems, contains over 700 functions. Whereas, it makes easier to navigate streets, talk on phone and more, this may lead to visual distraction, since when paying attention to a task not driving related, the brain focus on that activity. That distraction is, according to studies, the third cause of accidents, only surpassed by speeding and drunk driving. Driver distraction is stressed as the main concern by regulators, in particular, National Highway Transportation Safety Agency (NHTSA), which is developing recommended limits for the amount of time a driver needs to spend glancing away from the road to operate in-car features. Diverting attention from driving can be fatal; therefore, automakers have been challenged to design safer and comfortable human-machine interfaces (HMIs) without missing the latest technological achievements. This dissertation aims to mitigate driver distraction by developing a gestural recognition system that allows the user a more comfortable and intuitive experience while driving. The developed system outlines the algorithms to recognize gestures using the capacitive technology.Impulsionados pelos avanços tecnológicos, os automóveis tem de forma continua aumentado em complexidade, sobretudo na forma como os conductores e passageiros interagem com os seus veículos. Por exemplo, os sistemas controlados pelo condutor do BMW série 7 continham mais de 700 funções. Embora, isto facilite a navegação entre locais, falar ao telemóvel entre outros, isso pode levar a uma distração visual, já que ao prestar atenção a uma tarefa não relacionados com a condução, o cérebro se concentra nessa atividade. Essa distração é, de acordo com os estudos, a terceira causa de acidentes, apenas ultrapassada pelo excesso de velocidade e condução embriagada. A distração do condutor é realçada como a principal preocupação dos reguladores, em particular, a National Highway Transportation Safety Agency (NHTSA), que está desenvolvendo os limites recomendados para a quantidade de tempo que um condutor precisa de desviar o olhar da estrada para controlar os sistemas do carro. Desviar a atenção da conducção, pode ser fatal; portanto, os fabricante de automóveis têm sido desafiados a projetar interfaces homemmáquina (HMIs) mais seguras e confortáveis, sem perder as últimas conquistas tecnológicas. Esta dissertação tem como objetivo minimizar a distração do condutor, desenvolvendo um sistema de reconhecimento gestual que permite ao utilizador uma experiência mais confortável e intuitiva ao conduzir. O sistema desenvolvido descreve os algoritmos de reconhecimento de gestos usando a tecnologia capacitiva.It is worth noting that this work has been financially supported by the Portugal Incentive System for Research and Technological Development in scope of the projects in co-promotion number 036265/2013 (HMIExcel 2013-2015), number 002814/2015 (iFACTORY 2015-2018) and number 002797/2015 (INNOVCAR 2015-2018)

Convolutional neural networks for hand gesture recognition with off-the-shelf radar sensor

openL'elaborato espone l'attività di ricerca riguardante l'applicazione di metodologie di Machine Learning per risolvere un problema di interazione uomo-macchina. L'obiettivo è riconoscere e classificare correttamente dei movimenti della mano eseguiti da un utente, i quali vengono catturati tramite un sensore radar. Il segnale viene successivamente processato e dato in input ad una rete neurale convoluzionale, seguita da un classificatore volto a riconoscere il movimento che viene eseguito.The thesis explains the research and metholodogies applied in order to solve a Human-Computer Interaction task by means of Machine Learning techniques. The goal is to recognize and classify hand gestures performed by the user, which are acquired with a radar sensor. The signal is then processed and given as input to a convolutional neural network, followed by a fully connected classifier that should be able to classify correctly the movement