Securing internet of medical things with friendly-jamming schemes

The Internet of Medical Things (IoMT)-enabled e-healthcare can complement traditional medical treatments in a flexible and convenient manner. However, security and privacy become the main concerns of IoMT due to the limited computational capability, memory space and energy constraint of medical sensors, leading to the in-feasibility for conventional cryptographic approaches, which are often computationally-complicated. In contrast to cryptographic approaches, friendly jamming (Fri-jam) schemes will not cause extra computing cost to medical sensors, thereby becoming potential countermeasures to ensure security of IoMT. In this paper, we present a study on using Fri-jam schemes in IoMT. We first analyze the data security in IoMT and discuss the challenges. We then propose using Fri-jam schemes to protect the confidential medical data of patients collected by medical sensors from being eavesdropped. We also discuss the integration of Fri-jam schemes with various communication technologies, including beamforming, Simultaneous Wireless Information and Power Transfer (SWIPT) and full duplexity. Moreover, we present two case studies of Fri-jam schemes in IoMT. The results of these two case studies indicate that the Fri-jam method will significantly decrease the eavesdropping risk while leading to no significant influence on legitimate transmission

Intelligent ultrasound hand gesture recognition system

With the booming development of technology, hand gesture recognition has become a hotspot in Human-Computer Interaction (HCI) systems. Ultrasound hand gesture recognition is an innovative method that has attracted ample interest due to its strong real-time performance, low cost, large field of view, and illumination independence. Well-investigated HCI applications include external digital pens, game controllers on smart mobile devices, and web browser control on laptops. This thesis probes gesture recognition systems on multiple platforms to study the behavior of system performance with various gesture features. Focused on this topic, the contributions of this thesis can be summarized from the perspectives of smartphone acoustic field and hand model simulation, real-time gesture recognition on smart devices with speed categorization algorithm, fast reaction gesture recognition based on temporal neural networks, and angle of arrival-based gesture recognition system. Firstly, a novel pressure-acoustic simulation model is developed to examine its potential for use in acoustic gesture recognition. The simulation model is creating a new system for acoustic verification, which uses simulations mimicking real-world sound elements to replicate a sound pressure environment as authentically as possible. This system is fine-tuned through sensitivity tests within the simulation and validate with real-world measurements. Following this, the study constructs novel simulations for acoustic applications, informed by the verified acoustic field distribution, to assess their effectiveness in specific devices. Furthermore, a simulation focused on understanding the effects of the placement of sound devices and hand-reflected sound waves is properly designed. Moreover, a feasibility test on phase control modification is conducted, revealing the practical applications and boundaries of this model. Mobility and system accuracy are two significant factors that determine gesture recognition performance. As smartphones have high-quality acoustic devices for developing gesture recognition, to achieve a portable gesture recognition system with high accuracy, novel algorithms were developed to distinguish gestures using smartphone built-in speakers and microphones. The proposed system adopts Short-Time-Fourier-Transform (STFT) and machine learning to capture hand movement and determine gestures by the pretrained neural network. To differentiate gesture speeds, a specific neural network was designed and set as part of the classification algorithm. The final accuracy rate achieves 96% among nine gestures and three speed levels. The proposed algorithms were evaluated comparatively through algorithm comparison, and the accuracy outperformed state-of-the-art systems. Furthermore, a fast reaction gesture recognition based on temporal neural networks was designed. Traditional ultrasound gesture recognition adopts convolutional neural networks that have flaws in terms of response time and discontinuous operation. Besides, overlap intervals in network processing cause cross-frame failures that greatly reduce system performance. To mitigate these problems, a novel fast reaction gesture recognition system that slices signals in short time intervals was designed. The proposed system adopted a novel convolutional recurrent neural network (CRNN) that calculates gesture features in a short time and combines features over time. The results showed the reaction time significantly reduced from 1s to 0.2s, and accuracy improved to 100% for six gestures. Lastly, an acoustic sensor array was built to investigate the angle information of performed gestures. The direction of a gesture is a significant feature for gesture classification, which enables the same gesture in different directions to represent different actions. Previous studies mainly focused on types of gestures and analyzing approaches (e.g., Doppler Effect and channel impulse response, etc.), while the direction of gestures was not extensively studied. An acoustic gesture recognition system based on both speed information and gesture direction was developed. The system achieved 94.9% accuracy among ten different gestures from two directions. The proposed system was evaluated comparatively through numerical neural network structures, and the results confirmed that incorporating additional angle information improved the system's performance. In summary, the work presented in this thesis validates the feasibility of recognizing hand gestures using remote ultrasonic sensing across multiple platforms. The acoustic simulation explores the smartphone acoustic field distribution and response results in the context of hand gesture recognition applications. The smartphone gesture recognition system demonstrates the accuracy of recognition through ultrasound signals and conducts an analysis of classification speed. The fast reaction system proposes a more optimized solution to address the cross-frame issue using temporal neural networks, reducing the response latency to 0.2s. The speed and angle-based system provides an additional feature for gesture recognition. The established work will accelerate the development of intelligent hand gesture recognition, enrich the available gesture features, and contribute to further research in various gestures and application scenarios