Wearable Wristworn Gesture Recognition Using Echo State Network

This paper presents a novel gesture sensing system for prosthetic limb control based on a pressure sensor array embedded in a wristband. The tendon movement which produces pressure change around the wrist can be detected by pressure sensors. A microcontroller is used to gather the data from the sensors, followed by transmitting the data into a computer. A user interface is developed in LabVIEW, which presents the value of each sensor and display the waveform in real-time. Moreover, the data pattern of each gesture varies from different users due to the non-uniform subtle tendon movement. To overcome this challenge, Echo State Network (ESN), a supervised learning network, is applied to the data for calibrating different users. The results of gesture recognition show that the ESN has a good performance in multiple dimensional classifications. For experimental data collected from six participants, the proposed system classifies five gestures with an accuracy of 87.3%

A Delay-Based Neuromorphic Processor for Arrhythmias Detection

Cardiovascular disease is the leading cause of global mortality, with 17.5 Million deaths per annum (World Health Authority, WHO). Innovative hardware based cardiac recording devices could help elevate this burden. Delay-based reservoir computing is a novel computational framework with only a single nonlinear node. This feature makes it a strong candidate for the hardware implementation of an analogue cognitive system. Such a system can be exploited to improve the energy efficiency of data processing in implantable bioelectronic devices. This paper presents a system modelling of this network that is capable of cognitively processing Electrocardiograph (ECG) signals from the MIT-BIH arrhythmia database. The proposed single-input single-output model receives an encoded ECG signal while the output amplitude pattern aids the diagnostic interpretation. The information processor is an analogue circuit with the dynamic properties of Mackey-Glass nonlinearity and fading memory. To validate this system and mimic real-time operation, the simulation is designed to detect ventricular ectopic beats, an ectopic heartbeat type, using a continuous ECG signal without any signal segmentation or feature extraction. After training, the model successfully locates ventricular ectopic beat with 87.51% sensitivity and 94.12% accuracy for the testing dataset from three patients

Physical reservoir computing with dynamical electronics

Since the advent of data-driven society, mass information generated from human activity and the natural environment has been collected, stored, processed, and then dispersed under conventional von Neumann architecture. However, further scaling the computing capability in terms of speed and power efficiency has been significantly slowed down in recent years due to the fundamental limits of transistors. To meet the increasingly demanding requirement for data-intensive computation, neuromorphic computing is a promising field taking the inspiration from the human brain, an extremely efficient biological computer, to develop unconventional computing paradigms for artificial intelligence. Reservoir computing, a recurrent neural network algorithm invented two decades ago, has received wide attention in the field of neuromorphic computing because of its unique recurrent dynamics and hardware-friendly implementation schemes. Under the concept of reservoir computing, hardware’s intrinsic physical behaviours can be explored as computing resources to keep the machine learning within the physical domain to improve processing efficiency, which is also known as physical reservoir computing. This thesis focuses on modelling and implementing physical reservoir computing based on dynamical electronics, along with its applications with sensory signals. First, the fundamental of the reservoir computing algorithm is introduced. Second, based on the reservoir algorithm and its functionalities, two different architectures for physically implementing reservoir computing, delay-based reservoir and parallel devices, are investigated to perform temporal signal processing. Thirdly, an efficient implementation architecture, namely rotating neurons reservoir, is developed. This novel architecture is evaluated in both theoretical analysis and experiments. An electrical prototype of the rotating neurons reservoir exhibits unique advantages such as resource-efficient implementation and low power consumption. More importantly, the theory of rotating neurons reservoir is highly universal, indicating that a rotational object embedded with dynamical elements can act as a reservoir computer

A neuromorphic model with delay-based reservoir for continuous ventricular heartbeat detection

There is a growing interest in neuromorphic hardware since it oers a more intuitive way to achieve bio-inspired algorithms. This paper presents a neuromorphic model for intelligently processing continuous electrocardiogram (ECG) signals. This model aims to develop a hardware-based signal processing model and avoid employing digitally intensive operations, such as signal segmentation and feature extraction, which are not desired in an analogue neuromorphic system. We apply delay-based reservoir computing as the information processing core, along with a novel training and labelling method. Dierent from the conventional ECG classication techniques, this computation model is an end-to-end dynamic system that mimics the real-time signal ow in neuromorphic hardware. The input is the raw ECG stream, while the amplitude of the output represents the risk factor of a ventricular ectopic heartbeat. The intrinsic memristive property of the reservoir empowers the system to retain the historical ECG information for high-dimensional mapping. This model was evaluated with the MIT-BIH database under the inter-patient paradigm and yields 81% sensitivity and 98% accuracy. Under this architecture, the minimum size of memory required in the inference process can be as low as 3.1 MegaByte(MB) because the majority of the computation takes place in the analogue domain. Such computational modelling boosts memory eciency by simplifying the computing procedure and minimizing the required memory for future wearable devices