Dynamic Hand Gesture Classification Based on Radar Micro-Doppler Signatures

Dynamic hand gesture recognition is of great importance for human-computer interaction. In this paper, we present a method to discriminate the four kinds of dynamic hand gestures, snapping fingers, flipping fingers, hand rotation and calling, using a radar micro-Doppler sensor. Two micro-Doppler features are extracted from the time-frequency spectrum and the support vector machine is used to classify these four kinds of gestures. The experimental results on measured data demonstrate that the proposed method can produce a classification accuracy higher than 88.56%

Effect of sparsity-aware time–frequency analysis on dynamic hand gesture classification with radar micro-Doppler signatures

Dynamic hand gesture recognition is of great importance in human-computer interaction. In this study, the authors investigate the effect of sparsity-driven time-frequency analysis on hand gesture classification. The time-frequency spectrogram is first obtained by sparsity-driven time-frequency analysis. Then three empirical micro-Doppler features are extracted from the time-frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time-frequency analysis techniques, sparsity-driven time-frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification

Exploração de radar para reconhecimento de gestos

Communication disorders have a notable negative impact on people’s lives, leading to isolation, depression and loss of independence. Over the years, many different approaches to attenuate these problems were proposed, although most come with noticeable drawbacks. Lack of versatility, intrusive solutions or the need to carry a device around are some of the problems that these solutions encounter. Radars have seen an increase in use over the past few years and even spreading to different areas such as the automotive and health sectors. This technology is non-intrusive, not sensitive to changes in environmental conditions such as lighting, and does not intrude on the user’s privacy unlike cameras. In this dissertation and in the scope of the APH-ALARM project, the author tests the radar in a gesture recognition context to support communication in the bedroom scenario. In this scenario, the user is someone with communication problems, lying in their bed trying to communicate with a family member inside or outside the house. The use of gestures allows the user to have assistance communicating and helps express their wants or needs. To recognize the gestures executed by the user, it is necessary to capture the movement. To demonstrate the capabilities of the technology, a proof of concept system was implemented, which captures the data, filters and transforms it into images used as input for a gesture classification model. To evaluate the solution, we recorded ten repetitions of five arm gestures executed by four people. A subject independent solution proved to be more challenging when compared to a subject dependent solution, where all datasets but one achieved a median accuracy above 70% with most going over 90%.Os problemas de comunicação têm um efeito nocivo nas vidas das pessoas como isolamento, depressão e perda de independência. Ao longo dos anos, várias abordagens para atenuar estes problemas foram propostas, sendo que a maioria tem desvantagens. Falta de versatilidade, soluções intrusivas ou a necessidade de andar com um dispositivo são alguns dos problemas destas soluções. O uso de radares tem visto um aumento nos últimos anos, chegando até áreas variadas como o setor de saúde ou automóvel. Este tipo de solução é não intrusiva, não é sensível a mudanças das condições ambientais como luz e não invade a privacidade do utilizador como o uso de câmaras. Nesta dissertação e no âmbito do projeto APH-ALARM, testou-se um radar no contexto do reconhecimento de gestos para apoio à comunicação no cenário do quarto. Neste cenário, o utilizador é alguém com problemas de comunicação, que se encontra deitado na sua cama e precisa de comunicar com um familiar dentro ou fora de casa. O uso de gestos permite ao utilizador ter algum apoio durante a comunicação e ajuda o mesmo a expressar as suas necessidades. Para reconhecer os gestos feitos pelo utilizador, é necessário capturar o movimento humano. Para demonstrar as capacidades da tecnologia para este contexto, foi implementada uma prova de conceito de um sistema que captura os dados do radar e de seguida os filtra, converte-os em imagens e usa as mesmas como entrada de um modelo para classificação de gestos. Para avaliar a solução proposta, foram recolhidos dados de quatro pessoas enquanto realizavam dez repetições de cinco gestos diferentes com um dos braços. Uma solução independente do utilizador mostrou ser um caso mais desafiante quando comparada com uma solução dependente do utilizador, em que todos os datasets excepto um tem um acerto médio superior a 70% em que a maioria deles supera os 90%.Mestrado em Engenharia de Computadores e Telemátic

Contactless WiFi Sensing and Monitoring for Future Healthcare:Emerging Trends, Challenges and Opportunities

WiFi sensing has recently received significant interest from academics, industry, healthcare professionals and other caregivers (including family members) as a potential mechanism to monitor our aging population at distance, without deploying devices on users bodies. In particular, these methods have gained significant interest to efficiently detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems stems from its practical deployments in indoor settings and compliance from monitored persons, unlike other sensors such as wearables, camera-based, and acoustic-based solutions. This paper reviews state-of-the-art research on collecting and analysing channel state information, extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, untapped areas, and related trends.This work aims to provide an overarching view in understanding the technology and discusses its uses-cases from a perspective that considers hardware, advanced signal processing, and data acquisition

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

Wi-Fi Sensing: Applications and Challenges

Wi-Fi technology has strong potentials in indoor and outdoor sensing applications, it has several important features which makes it an appealing option compared to other sensing technologies. This paper presents a survey on different applications of Wi-Fi based sensing systems such as elderly people monitoring, activity classification, gesture recognition, people counting, through the wall sensing, behind the corner sensing, and many other applications. The challenges and interesting future directions are also highlighted

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