Wearable electronics is a rapidly growing field that recently started to introduce successful commercial products into the consumer electronics market. Employment of biopotential signals in wearable systems as either biofeedbacks or control commands are expected to revolutionize many technologies including point of care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMIs), and brain–computer interfaces (BCIs). Since electrodes are regarded as a decisive part of such products, they have been studied for almost a decade now, resulting in the emergence of textile electrodes. This study presents a systematic review of wearable textile electrodes in physiological signal monitoring, with discussions on the manufacturing of conductive textiles, metrics to assess their performance as electrodes, and an investigation of their application in the acquisition of critical biopotential signals for routine monitoring, assessment, and exploitation of cardiac (electrocardiography, ECG), neural (electroencephalography, EEG), muscular (electromyography, EMG), and ocular (electrooculography, EOG) functions.

Wearable graphene textile embedded smart headband and its feasibility in electrooculography (EOG) applications is demonstrated by benchmarking against clinical Ag/AgCl wet electrodes; where the recorded biopotentials displayed excellent correlation of 91.3% over durations up to hundred seconds. Automatic eye movement (EM) detection is implemented and performance of the grapheneembedded "all-textile" eye movement sensor and its application as a control element toward human-computer interaction (HCI) and human-machine interfaces (HMI) is experimentally demonstrated by: 1) generating digital clock transitions directly from eye blinks for facilitating switching requirements in HCI/HMI applications, 2) controlling and sequentially lighting up a single LED in a 5 × 5 LED array in four directions to draw a pattern of "8", 3) evaluating the limits of the entire system in an hour-long EOG recording session which includes several activities like checking a phone, watching a video, reading, and performing several EMs including blinks, saccades, and fixations. The excellent success rate ranging from 85% up to 100% for eleven different EM patterns demonstrates the applicability of the proposed algorithm in wearable EOG-based sensing and HCI/HMI applications with graphene textiles. The system-level integration and the holistic design approach presented herein which starts from fundamental materials level up to the architecture and algorithm stage is highlighted and will be instrumental to advance the state-of-the-art in wearable electronic devices.

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The study of eye movements and the measurement of the resulting biopotential, referred to as electrooculography (EOG), may find increasing use in applications within the domain of activity recognition, context awareness, mobile human–computer and human–machine interaction (HCI/HMI), and personal medical devices; provided that, seamless sensing of eye activity and processing thereof is achieved by a truly wearable, low-cost, and accessible technology. The present study demonstrates an alternative to the bulky and expensive camera-based eye tracking systems and reports the development of a graphene textile-based personal assistive device for the first time. This self-contained wearable prototype comprises a headband with soft graphene textile electrodes that overcome the limitations of conventional “wet” electrodes, along with miniaturized, portable readout electronics with real-time signal processing capability that can stream data to a remote device over Bluetooth. The potential of graphene textiles in wearable eye tracking and eye-operated remote object interaction is demonstrated by controlling a mouse cursor on screen for typing with a virtual keyboard and enabling navigation of a four-wheeled robot in a maze, all utilizing five different eye motions initiated with a single channel EOG acquisition. Typing speeds of up to six characters per minute without prediction algorithms and guidance of the robot in a maze with four 180° turns were successfully achieved with perfect pattern detection accuracies of 100% and 98%, respectively.

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