Systematic review of textile-based electrodes for long-term and continuous surface electromyography recording

This systematic review concerns the use of smart textiles enabled applications based on myoelectric activity. Electromyography (EMG) is the technique for recording and evaluating electric signals related to muscle activity (myoelectric). EMG is a well-established technique that provides a wealth of information for clinical diagnosis, monitoring, and treatment. Introducing sensor systems that allow for ubiquitous monitoring of health conditions using textile integrated solutions not only opens possibilities for ambulatory, long-term, and continuous health monitoring outside the hospital, but also for autonomous self-administration. Textile-based electrodes have demonstrated potential as a fully operational alternative to \u27standard\u27 Ag/AgCl electrodes for recording surface electromyography (sEMG) signals. As a substitute for Ag/AgCl electrodes fastened to the skin by taping or pre-gluing adhesive, textile-based electrodes have the advantages of being soft, flexible, and air permeable; thus, they have advantages in medicine and health monitoring, especially when self-administration, real-time, and long-term monitoring is required. Such advances have been achieved through various smart textile techniques; for instance, adding functions in textiles, including fibers, yarns, and fabrics, and various methods for incorporating functionality into textiles, such as knitting, weaving, embroidery, and coating. In this work, we reviewed articles from a textile perspective to provide an overview of sEMG applications enabled by smart textile strategies. The overview is based on a literature evaluation of 41 articles published in both peer-reviewed journals and conference proceedings focusing on electrode materials, fabrication methods, construction, and sEMG applications. We introduce four textile integration levels to further describe the various textile electrode sEMG applications reported in the reviewed literature. We conclude with suggestions for future work along with recommendations for the reporting of essential benchmarking information in current and future textile electrode applications

Digital Sensing Systems for Electromyography

Surface electromyogram (EMG) signals find diverse applications in movement rehabilitation and human-computer interfacing. For instance, future advanced prostheses, which use artificial intelligence, will require EMG signals recorded from several sites on the forearm. This requirement will entail complex wiring and data handling. We present the design and evaluation of a bespoke EMG sensing system that addresses the above challenges, enables distributed signal processing, and balances local versus global power consumption. Additionally, the proposed EMG system enables the recording and simultaneous analysis of skin-sensor impedance, needed to ensure signal fidelity. We evaluated the proposed sensing system in three experiments, namely, monitoring muscle fatigue, real-time skin-sensor impedance measurement, and control of a myoelectric computer interface. The proposed system offers comparable signal acquisition characteristics to that achieved by a clinically-approved product. It will serve and integrate future myoelectric technology better via enabling distributed machine learning and improving the signal transmission efficiency

A multichannel wireless sEMG sensor endowing a 0.13 μm CMOS mixed-signal SoC

This paper presents a wireless multichannel surface electromyography (sEMG) sensor which features a custom 0.13μm CMOS mixed-signal system-on-chip (SoC) analog frontend circuit. The proposed sensor includes 10 sEMG recording channels with tunable bandwidth (BW) and analog-to-digital converter (ADC) resolution. The SoC includes 10x bioamplifiers, 10x 3 rd order ΔΣ MASH 1-1-1 ADC, and 10x on-chip decimation filters (DF). This SoC provides the sEMG samples data through a serial peripheral interface (SPI) bus to a microcontroller unit (MCU) that then transfers the data to a wireless transceiver. We report sEMG waveforms acquired using a custom multichannel electrode module, and a comparison with a commercial grade system. Results show that the proposed integrated wireless SoC-based system compares well with the commercial grade sEMG recording system. The sensor has an input-referred noise of 2.5 μVrms (BW of 10-500 Hz), an input-dynamic range of 6 mVpp, a programmable sampling rate of 2 ksps, for sEMG, while consuming only 7.1 μW/Ch for the SoC (w/ ADC & DF) and 21.8 mW of power for the sensor (Transceiver, MCU, etc.). The system lies on a 1.5 × 2.0 cm 2 printed circuit board and weights <; 1 g

A low-cost, wireless, 3-D-printed custom armband for sEMG hand gesture recognition

Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be derived by leveraging machine learning. However, the sEMG acquisition systems currently available tend to be prohibitively costly for personal use or sacrifice wearability or signal quality to be more affordable. This work introduces the 3DC Armband designed by the Biomedical Microsystems Laboratory in Laval University; a wireless, 10-channel, 1000 sps, dry-electrode, low-cost ( 150 USD) myoelectric armband that also includes a 9-axis inertial measurement unit. The proposed system is compared with the Myo Armband by Thalmic Labs, one of the most popular sEMG acquisition systems. The comparison is made by employing a new offline dataset featuring 22 able-bodied participants performing eleven hand/wrist gestures while wearing the two armbands simultaneously. The 3DC Armband systematically and significantly (p < 0.05) outperforms the Myo Armband, with three different classifiers employing three different input modalities when using ten seconds or more of training data per gesture. This new dataset, alongside the source code, Altium project and 3-D models are made readily available for download within a Github repository

Intimate interfaces in action: assessing the usability and subtlety of emg-based motionless gestures

Mobile communication devices, such as mobile phones and networked personal digital assistants (PDAs), allow users to be constantly connected and communicate anywhere and at any time, often resulting in personal and private communication taking place in public spaces. This private -- public contrast can be problematic. As a remedy, we promote intimate interfaces: interfaces that allow subtle and minimal mobile interaction, without disruption of the surrounding environment. In particular, motionless gestures sensed through the electromyographic (EMG) signal have been proposed as a solution to allow subtle input in a mobile context. In this paper we present an expansion of the work on EMG-based motionless gestures including (1) a novel study of their usability in a mobile context for controlling a realistic, multimodal interface and (2) a formal assessment of how noticeable they are to informed observers. Experimental results confirm that subtle gestures can be profitably used within a multimodal interface and that it is difficult for observers to guess when someone is performing a gesture, confirming the hypothesis of subtlety

Graphene textile smart clothing for wearable cardiac monitoring

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 reports on the synthesis and application of graphene nanotextiles for the development of wearable electrocardiography (ECG) sensors for personalized health monitoring applications. In this study, we show for the first time that the electrocardiogram was successfully obtained with graphene textiles placed on a single arm. The use of only one elastic armband, and an “all-textile-approach” facilitates seamless heart monitoring with maximum comfort to the wearer. The functionality of graphene textiles produced using dip coating and stencil printing techniques has been demonstrated by the non-invasive measurement of ECG signals, up to 98% excellent correlation with conventional pre-gelled, wet, silver/silver-chloride (Ag / AgCl) electrodes. Heart rate have been successfully determined with ECG signals obtained in different situations. The system-level integration and holistic design approach presented here will be effective for developing the latest technology in wearable heart monitoring devices

Biosignal‐based human–machine interfaces for assistance and rehabilitation : a survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal‐based HMIs for assistance and rehabilitation to outline state‐of‐the‐art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full‐text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever‐growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complex-ity, so their usefulness should be carefully evaluated for the specific application

Designing Embroidered Electrodes for Wearable Surface Electromyography

This work was supported by the UK Crafts Council as part of the Parallel Practices project, by the Seventh Framework Programme of the European Commission under grant agreement 287728 in the framework of EU project STIFF-FLOP and by the Horizon 2020 Research and Innovation Programme under grant agreement 637095 in the framework of EU project FourByThree