Spintronic Eyeblink Gesture Sensor With Wearable Interface System.

This work presents an eyeblink system that detects magnets placed on the eyelid via integrated magnetic sensors and an analogue circuit on an eyewear frame (without a glass lens). The eyelid magnets were detected using tunnelling magnetoresistance (TMR) bridge sensors with a sensitivity of 14 mV/V/Oe and were positioned centre-right and centre-left of the eyewear frame. Each eye side has a single TMR sensor wired to a single circuit, where the signal was filtered (<0.5 Hz and >30 Hz) and amplified to detect the weak magnetic field produced by the 3-millimetre (mm) diameter and 0.5 mm thickness N42 Neodymium magnets attached to a medical tape strip, for the adult-age demographic. Each eyeblink was repeated by a trigger command (right eyeblink) followed by the appropriate command, right, left or both eyeblinks. The eyeblink gesture system has shown repeatability, resulting in blinking classification based on the analogue signal amplitude threshold. As a result, the signal can be scaled and classified as well as, integrated with a Bluetooth module in real-time. This will enable end-users to connect to various other Bluetooth enabled devices for wireless assistive technologies. The eyeblink system was tested by 14 participants via a stimuli-based game. Within an average time of 185-seconds, the system demonstrated a group mean accuracy of 72% for 40 commands. Moreover, the maximum information transfer rate (ITR) of the participants was 35.95 Bits per minute.

Rotating neurons for all-analog implementation of cyclic reservoir computing.

Hardware implementation in resource-efficient reservoir computing is of great interest for neuromorphic engineering. Recently, various devices have been explored to implement hardware-based reservoirs. However, most studies were mainly focused on the reservoir layer, whereas an end-to-end reservoir architecture has yet to be developed. Here, we propose a versatile method for implementing cyclic reservoirs using rotating elements integrated with signal-driven dynamic neurons, whose equivalence to standard cyclic reservoir algorithm is mathematically proven. Simulations show that the rotating neuron reservoir achieves record-low errors in a nonlinear system approximation benchmark. Furthermore, a hardware prototype was developed for near-sensor computing, chaotic time-series prediction and handwriting classification. By integrating a memristor array as a fully-connected output layer, the all-analog reservoir computing system achieves 94.0% accuracy, while simulation shows >1000× lower system-level power than prior works. Therefore, our work demonstrates an elegant rotation-based architecture that explores hardware physics as computational resources for high-performance reservoir computing.

A Neuromorphic Model With Delay-Based Reservoir for Continuous Ventricular Heartbeat Detection.

There is a growing interest in neuromorphic hardware since it offers a more intuitive way to achieve bio-inspired algorithms. This paper presents a neuromorphic model for intelligently processing continuous electrocardiogram (ECG) signal. 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. Different from the conventional ECG classification techniques, this computation model is a end-to-end dynamic system that mimics the real-time signal flow 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 efficiency by simplifying the computing procedure and minimizing the required memory for future wearable devices.

Adaptive Extreme Edge Computing for Wearable Devices.

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

Spintronic Sensors Based on Magnetic Tunnel Junctions for Wireless Eye Movement Gesture Control.

The tracking of eye gesture movements using wearable technologies can undoubtedly improve quality of life for people with mobility and physical impairments by using spintronic sensors based on the tunnel magnetoresistance (TMR) effect in a human-machine interface. Our design involves integrating three TMR sensors on an eyeglass frame for detecting relative movement between the sensor and tiny magnets embedded in an in-house fabricated contact lens. Using TMR sensors with the sensitivity of 11 mV/V/Oe and ten <1 mm3 embedded magnets within a lens, an eye gesture system was implemented with a sampling frequency of up to 28 Hz. Three discrete eye movements were successfully classified when a participant looked up, right or left using a threshold-based classifier. Moreover, our proof-of-concept real-time interaction system was tested on 13 participants, who played a simplified Tetris game using their eye movements. Our results show that all participants were successful in completing the game with an average accuracy of 90.8%.