Electromyography Sensors Research Articles

A Lower-Back Exoskeleton With a Four-Bar Linkage Structure for Providing Extensor Moment and Lumbar Traction Force

Lower back pain and related injuries are prevalent and serious problems in various industries, and high compression force to the lumbosacral (L5/S1) region has been known as one of the key factors. Previous research on passive lower back exoskeletons focused on reducing lumbar muscle activation by providing an extensor moment. Additionally, lumbar traction forces can reduce the compression force, and is a common treatment method for lower back pain in clinics. In this paper, we propose a novel passive lower back exoskeleton that provides both extensor moment and lumbar traction force. The working principle of the exoskeleton, extending the coil springs during lumbar flexion, and its design criteria regarding the amount of each force element were provided. The kinematic model explained its operation, and the dynamic simulation estimated its performance and validated its satisfaction with the design criteria. The biomechanical model provided a brief insight into the expected exoskeleton's effect on the reduced lower back compression force. Ten subjects performed static holding and dynamic lifting tasks, and the generated force elements in two directions, parallel and perpendicular to the trunk, were evaluated using a force sensor and electromyography sensors, respectively. The experiment demonstrated a pulling force opposite to the direction of intradiscal pressure and reduced erector spinae activation. This implies the effect of wearing the exoskeleton to decrease the intervertebral pressure during static back bending or heavy lifting tasks.

Study of a Multi-modal Neurorobotic Prosthetic Arm Control System based on Recurrent Spiking Neural Network

The use of robotic arms in various fields of human endeavor has increased over the years, and with recent advancements in artificial intelligence enabled by deep learning, they are increasingly being employed in medical applications like assistive robots for paralyzed patients with neurological disorders, welfare robots for the elderly, and prosthesis for amputees. However, robot arms tailored towards such applications are resource-constrained. As a result, deep learning with conventional artificial neural network (ANN) which is often run on GPU with high computational complexity and high power consumption cannot be handled by them. Neuromorphic processors, on the other hand, leverage spiking neural network (SNN) which has been shown to be less computationally complex and consume less power, making them suitable for such applications. Also, most robot arms unlike living agents that combine different sensory data to accurately perform a complex task, use uni-modal data which affects their accuracy. Conversely, multi-modal sensory data has been demonstrated to reach high accuracy and can be employed to achieve high accuracy in such robot arms. This paper presents the study of a multi-modal neurorobotic prosthetic arm control system based on recurrent spiking neural network. The robot arm control system uses multi-modal sensory data from visual (camera) and electromyography sensors, together with spike-based data processing on our previously proposed R-NASH neuromorphic processor to achieve robust accurate control of a robot arm with low power. The evaluation result using both uni-modal and multi-modal input data show that the multi-modal input achieves a more robust performance at 87%, compared to the uni-modal.

Neuromechanical Responses to Spinal Manipulation and Mobilization: A Crossover Randomized Clinical Trial

ObjectiveThe purpose of this study was to compare the immediate effect of spinal manipulation (SMa) and spinal mobilization (SMo) on muscular responses, spinal stiffness, and segmental spinal pressure evoked pain in a population of participants with chronic middle back pain (MBP). MethodsIn a crossover randomized trial, 2 experienced chiropractors assessed whether volunteers were eligible for the protocol according to a list of specific inclusion and exclusion criteria. Individuals with MBP participated in 2 experimental sessions within 72 hours. During the first session, participants randomly received a SMa or SMo delivered by an apparatus using a servolinear motor. During the second session, the other modality was delivered. Spinal stiffness and pressure-provoked pain intensity outcomes were assessed before and after each therapy, and muscular responses were recorded during the treatment using surface electromyographic sensors. Signed-rank Wilcoxon tests for muscular responses and generalized model for repeated measure for spinal stiffness and pressure-provoked pain were used for statistical analyses. ResultsAmong the 32 potential participants, 26 (mean age 29.9 [±9.14], 15 women) completed both sessions. Between-group differences were observed for the muscular response amplitude (P < .001), and indeed the normalized RMS muscular response was found to be higher during SMa than SMo. Similar results were observed for pressure-provoked pain intensity at the level of therapeutic modality application (P = .002) as a higher decrease in pain was found after SMa (47.9 [±22.8] to 36.6 [±23.7]) compared with SMo (47.2 [±23.2] to 45.5 [±24.3]). No between-group differences were found for spinal stiffness change, nor for terminal (P = .08) and global spinal stiffness (P = .06). ConclusionIn a controlled environment, spinal manipulation and mobilization generated different muscle responses and had different immediate effects on pressure-provoked pain intensity for participants with MBP.

케틀벨 훈련이 유도 선수들의 케틀벨 스윙 근활성도 및 체력에 미치는 영향

The purpose of this study was to find out whether improvement was achieved by applying a kettlebell training program to improve the fundamental physical strength, lumbar muscle strength, and muscle power of judo athletes. The purpose of this study is to find out how the muscle activity that appears during kettlebell swings changes after training. Body composition (height, weight, body fat percentage) and physical strength (muscle endurance, flexibility, muscle strength, psoas, psoas power) were measured to measure primary and professional physical strength. After attaching the electromyography sensor, about ten kettlebell swings were performed continuously, and kettlebells weighing 12 kg and 16 kg were used. At this time, the kettlebell swing phase was divided into two phases. The first phase of the kettlebell swing was defined as the section from when the kettlebell went down to the front of the collarbone. Phase 2 was defined as the section from the front of the collarbone to the bottom of the kettlebell. As a result of the study, kettlebell training showed an increase in all items in essential physical fitness, but the two athletes showed different patterns in professional fitness and muscle activity. It shows the injury and lack of sufficient rest during training and the need for more extended kettlebell training. It is believed that more reliable training results would have appeared.

Muscle coordination during archery shooting: A comparison of archers with different skill levels

ABSTRACT This study aimed to compare the muscle coordination of different skill level archers by using the concept of muscle synergies. A total of 28 archers (8 elite, 12 mid-level, and 8 novices) were recruited to participate in this study. Electromyography (EMG) signals were recorded using a 13-channel (Trigno EMG sensor, Delsys Inc., USA) wireless surface EMG system. Fundamental synergies containing time-dependent activation coefficients (motor primitives) and time-invariant muscle weightings (motor modules) were extracted using non-negative matrix factorisation. We observed three fundamental synergies in all groups during archery shooting. The results showed that the centre of activity of the motor primitive of synergy-3 occurred earlier in novice archers than in elite and mid-level archers, which was followed by an increase in the averaged frequency of overlaps. The results also showed a slight difference in the relative muscle contribution of the motor module of synergy-2 and -3 within different groups. These findings revealed that the number of muscle synergies did not depend on the proficiency level; however, more expert archers improved timing management better than less experienced ones. Therefore, we suggest that coaches and athletes focus on optimising the temporal coordination of the follow-through phase.

Flexible and Wearable EMG and PSD Sensors Enabled Locomotion Mode Recognition for IoHT-Based In-Home Rehabilitation

Benefiting from the development of the Internet of Healthcare Things (IoHT) in recent years, locomotion mode recognition using wearable sensors plays a more and more important role in the field of in-home rehabilitation. In this paper, a smart sensing system utilizing flexible electromyography (EMG) sensors and a forty-eight-channel plantar stress distribution sensor for locomotion mode recognition is presented, together with its use under the IoHT architecture. EMG and plantar stress distribution (PSD) information from ten healthy subjects in five common locomotion modes in daily life were collected, analyzed, and then transmitted to remote end terminals (e.g., personal computers). The data analysis process was implemented with machine learning techniques, through which the locomotion modes were determined with a high accuracy of 96.53%. This article demonstrates a feasible means for accurate locomotion mode recognition by combining wearable sensing techniques and the machine learning algorithm, potentially advancing the development for IoHT based in-home rehabilitation.

Abstract 11194: Reducing Arm Muscle Activation by Using an Ablation Catheter Torque Tool: Electromyographic Analysis and User Perception

Background: Cardiac ablation catheters are small in diameter and require significant finger dexterity and grip strength to maintain stability and maneuver safely. We designed a catheter torque tool to reduce the muscle activation required to manipulate cardiac ablation catheters and improve user perception of task workload. Objective: The purpose of this study was to evaluate muscle usage and user perception when completing a simulated ablation task with and without the use of a novel ablation catheter torque tool. Methods: Participants, comprised of cardiology attendings and fellows, were fitted with surface electromyographic (EMG) sensors on 6 key muscle groups in the left hand and forearm. Maximum voluntary contraction (MVC) was collected for equal comparison among participants. A standard ablation catheter was inserted into a pediatric cardiac ablation simulator and subjects navigated the catheter tip to 6 specific electrophysiologic targets including a 1-minute simulated radiofrequency lesion. EMG activity normalized to percentage of MVC was collected throughout the task. The task was repeated 4 times, twice with and twice without the torque tool, in randomized order. A NASA Task Load Index (TLX) survey was completed by the participant at the conclusion each task. Results: Thirteen participants completed the study. Using Wilcoxon’s test, there was a significant difference (P &lt; 0.01) in mean percent of MVC in 4 of the 6 muscle groups when comparing bare catheter versus catheter with torque tool: abductor pollicis, brachioradialis, flexor carpi ulnaris, and extensor carpi radialis. The total expended energy for the abductor pollicis (P &lt; 0.01) and extensor carpi radialis (P = 0.02) were significantly reduced. Perceived physical demand, effort, and frustration were also significantly reduced when using the torque tool (P &lt; 0.01). Conclusions: Use of our novel catheter torque tool reduced the amount of muscle activation necessary to manipulate an ablation catheter during a simulated ablation task. Users’ perceived physical demand, effort, and frustration were less when the tool was used. This tool may improve catheter stability, increase procedure safety, and reduce operator stress and muscle fatigue when performing catheter ablation procedures.

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors.

Finger gesture recognition (FGR) plays a crucial role in achieving, for example, artificial limb control and human-computer interaction. Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG-based FGR is very popular and successful because surface EMG is a cumulative bioelectric signal from the surface of the skin that can accurately and intuitively represent the force of the fingers. However, existing surface EMG-based methods still cannot fully satisfy the required recognition accuracy for artificial limb control as the lack of high-precision sensor and high-accurate recognition model. To address this issue, this study proposes a novel FGR model that consists of sensing and classification of surface EMG signals (SC-FGR). In the proposed SC-FGR model, wireless sensors with high-precision surface EMG are first developed for acquiring multichannel surface EMG signals from the forearm. Its resolution is 16 Bits, the sampling rate is 2 kHz, the common-mode rejection ratio (CMRR) is less than 70 dB, and the short-circuit noise (SCN) is less than 1.5 μV. In addition, a convolution neural network (CNN)-based classification algorithm is proposed to achieve FGR based on acquired surface EMG signals. The CNN is trained on a spectrum map transformed from the time-domain surface EMG by continuous wavelet transform (CWT). To evaluate the proposed SC-FGR model, we compared it with seven state-of-the-art models. The experimental results demonstrate that SC-FGR achieves 97.5% recognition accuracy on eight kinds of finger gestures with five subjects, which is much higher than that of comparable models.

Locomotion Mode Transition Prediction Based on Gait-Event Identification Using Wearable Sensors and Multilayer Perceptrons.

People walk on different types of terrain daily; for instance, level-ground walking, ramp and stair ascent and descent, and stepping over obstacles are common activities in daily life. Movement patterns change as people move from one terrain to another. The prediction of transitions between locomotion modes is important for developing assistive devices, such as exoskeletons, as the optimal assistive strategies may differ for different locomotion modes. The prediction of locomotion mode transitions is often accompanied by gait-event detection that provides important information during locomotion about critical events, such as foot contact (FC) and toe off (TO). In this study, we introduce a method to integrate locomotion mode prediction and gait-event identification into one machine learning framework, comprised of two multilayer perceptrons (MLP). Input features to the framework were from fused data from wearable sensors—specifically, electromyography sensors and inertial measurement units. The first MLP successfully identified FC and TO, FC events were identified accurately, and a small number of misclassifications only occurred near TO events. A small time difference (2.5 ms and −5.3 ms for FC and TO, respectively) was found between predicted and true gait events. The second MLP correctly identified walking, ramp ascent, and ramp descent transitions with the best aggregate accuracy of 96.3%, 90.1%, and 90.6%, respectively, with sufficient prediction time prior to the critical events. The models in this study demonstrate high accuracy in predicting transitions between different locomotion modes in the same side’s mid- to late stance of the stride prior to the step into the new mode using data from EMG and IMU sensors. Our results may help assistive devices achieve smooth and seamless transitions in different locomotion modes for those with motor disorders.

Usability Assessment of Body Controlled Electric Hand Prostheses: A Pilot Study.

Poly-articulated hands, actuated by multiple motors and controlled by surface myoelectric technologies, represent the most advanced aids among commercial prostheses. However, simple hook-like body-powered solutions are still preferred for their robustness and control reliability, especially for challenging environments (such as those encountered in manual work or developing countries). This study presents the mechatronic implementation and the usability assessment of the SoftHand Pro-Hybrid, a family of poly-articulated, electrically-actuated, and body-controlled artificial hands, which combines the main advantages of both body-powered and myoelectric systems in a single device. An assessment of the proposed system is performed with individuals with and without limb loss, using as a benchmark the SoftHand Pro, which shares the same soft mechanical architecture, but is controlled using surface electromyographic sensors. Results indicate comparable task performance between the two control methods and suggest the potential of the SoftHand Pro-Hybrid configurations as a viable alternative to myoelectric control, especially in work and demanding environments.

Detecting lucid dreams only by submentalis electromyography

Lucid dreams (LDs) occur when people become aware that they are dreaming. This phenomenon has a wide range of possible applications from the perspectives of psychology, training physical movements, and controlling computers while asleep, among others. However, research on LDs might lack efficiency because the standard LD verification protocol uses polysomnography (PSG), which requires an expensive apparatus and skilled staff. The standard protocol also may reduce LD-induction efficiency. The current study examines whether humans can send phasic signals through submentalis electromyography (EMG) during muscle atonia via pre-agreed chin movements (PACM). This ability would manifest both REM sleep and consciousness, which are the main features of LDs. In laboratory conditions volunteers were instructed to open their jaws three times while in an LD right after the standard verification protocol to achieve the research goal. Results: 4 of 5 volunteers proved to be in an LD using the standard protocol, and then all of them made PACM. The outcomes show that dream signals cannot be blocked in the submentalis area during muscle atonia. Also, this finding can be considered to develop a simplified, reliable LD protocol that needs only one EMG sensor. The cost of this protocol could be only a small percentage of the current protocol, making it more convenient for researchers and volunteers. It can also be used remotely by inbuilt in wearable gadgets. Considering PACM could speed up LD research and provide many discoveries and new opportunities. Also, it can be used in sleep paralysis studies.

Gender recognition using optimal gait feature based on recursive feature elimination in normal walking

This study aims to propose a novel approach for gender recognition using best feature subset based on recursive feature elimination (RFE) in normal walking. This study has focused on the analysis of gait characteristics by distinguishing the gait phases as initial contact (IC), Mid-stance (MS), Pre-swing, and swing (SW), and collected the large number of gait to improve the reliability of quantitative assessment of natural variability associated with muscle activity during free walking. The gait system was designed using pressure and a tri-axis accelerometer sensor, and a 9-channel electromyography sensor for measuring the data. Gender recognition method was proposed using support vector machine (SVM) and random forest (RF) based on RFE to determine best feature subset. Statistical results show that effects of gender-based differences on gait characteristic including temporal, kinematics, and muscle activity were investigated. The temporal parameters of stride time and gait cycle (%) in the gait phases of IC, MS, and SW were significantly different between females and males (p<0.01). The females exhibited both a lower angle and a root mean square acceleration of the knee joint as compared to the males, and there was a clear gender-based difference with respect to knee angle movement. In addition, most muscle activation measurements in the females were larger than those of the males with respect to the gait phases. Gender classification result shows that SVM-RFE was 99.11% (SVM classifier) and RF-RFE was 98.89% (SVM and RF classifier), having powerful performance.

An Ultra-Low Power Surface EMG Sensor for Wearable Biometric and Medical Applications.

In recent years, the surface electromyography (EMG) signal has received a lot of attention. EMG signals are used to analyze muscle activity or to evaluate a patient’s muscle status. However, commercial surface EMG systems are expensive and have high power consumption. Therefore, the purpose of this paper is to implement a surface EMG acquisition system that supports high sampling and ultra-low power consumption measurement. This work analyzes and optimizes each part of the EMG acquisition circuit and combines an MCU with BLE. Regarding the MCU power saving method, the system uses two different frequency MCU clock sources and we proposed a ping-pong buffer as the memory architecture to achieve the best power saving effect. The measured surface EMG signal samples can be forwarded immediately to the host for further processing and additional application. The results show that the average current of the proposed architecture can be reduced by 92.72% compared with commercial devices, and the battery life is 9.057 times longer. In addition, the correlation coefficients were up to 99.5%, which represents a high relative agreement between the commercial and the proposed system.

Acupuncture Efficacy Study Using Electromyographic Signals

Acupuncture is a centuries-old therapeutic technique. However, because of the large number of complicating circumstances, it has been difficult to clearly prove the treatment's therapeutic effectiveness.As a result, acupuncture has failed to acquire acceptance in the mainstream clinical sector. An electromyography (EMG) sensor was built and used to test the efficacy of acupuncture in alleviating muscular stiffness in this study. Electrodes, differential and inverting amplifiers, filters, and a full-wave rectifier made up the EMG circuit. The output of the circuit was sent to a microcontroller for analog-to-digital transformation in order to perform data acquisition. Acupuncture was used to treat four participants who had muscular dysfunction in various regions of their bodies in our case study. Before and after the therapy, EMG signals at the damaged regions were recorded. The findings revealed that the therapy had no immediate conceivable impact on the patients, since the levels of muscular contraction before and after the treatment were comparable. When the EMG signals were measured 30 minutes after the therapy, signs of muscular alleviation were found. This shows that acupuncture does supply patients with beneficial medicine, although slowly. The act of placing the highly conducting needles into the acupuncture sites, we believe, is similar to connecting a parallel wire to a circuit, resulting in a short-circuited route at the meridian. It permits the meridian's polarized in- ner energy, or qi, to pass through. The equilibrium in qi regulation can therefore be restored by unclogging the ow of qi.The repair process is relatively slow, and the treatment impact may not be immediately apparent, because the consti- tutive qualities at the acupuncture points where the needles are pricked may not alter quickly.

Analysis and Recognition of Human Lower Limb Motions Based on Electromyography (EMG) Signals

Background: This paper focuses on the characteristics of lower limb EMG signals for common movements. Methods: We obtained length data for lower limb muscles during gait motion using software named OpenSim; statistical product and service solutions (SPSS) were utilized to study the correlation between each muscle, based on gait data. Low-correlation muscles in different regions were selected; inertial measurement unit (IMU) and EMG sensors were used to measure the lower limb angles and EMG signals when on seven kinds of slope, in five kinds of gait (walking on flat ground, uphill, downhill, up-step and down-step) and four kinds of movement (squat, lunge, raised leg and standing up). Results: After data denoising and feature extraction, we designed a double hidden-layer BP neural network to recognize the above motions according to EMG signals. Results show that EMG signals of selected muscles have a certain periodicity in the process of movement that can be used to identify lower limb movements. Conclusions: It can be seen, after the recognition of different proportions of training and testing sets that the average recognition rate of the BP neural network is 86.49% for seven gradients, 93.76% for five kinds of gait and 86.07% for four kinds of movements.

Non-lethal flashbang effectiveness pilot study: Acoustic effects

Flashbang grenades distract and temporarily disable an adversary, help gain surprise advantage, or disrupt communication in physical confrontations. Such devices are designed to emit high-amplitude acoustic signals, abright light, and/or a concussive force. This pilot study evaluated the acoustic effects that accompany the detonation of these devices, namely temporary threshold shift (TTS), the acoustic startle reflex, and temporary noise-induced tinnitus. In this study, human subjects were evaluated on two tasks: (1) target localization and (2) speech-in-noise (SIN) testing. During these tasks, subjects underwent a random sequence of acoustic startle, simulated TTS, and simulated tinnitus conditions. While response accuracy and latency was monitored during these tasks, subjects’ physical startle was also evaluated with electromyography (EMG) sensors, measuring muscle activity of the eyelid, jaw, neck, and bicep. Results suggest that each independent condition affects performance on localization and SIN tasks, with maximum suppressive effect occurring with all three main acoustic effects combined. As anticipated, the acoustic startle had a profound impact on the performance of at least some of subjects’ listening ability in this pilot. Further work is necessary to fully characterize these effects in pursuit of creating a non-lethal flashbang device.

Voluntary Muscle Contraction Detection Algorithm Based on LSTM for Muscle Quality Measurement Algorithm

In this paper, we propose the long–short-term memory (LSTM)-based voluntary and non-voluntary (VNV) muscle contraction classification algorithm in an electrical stimulation (ES) environment. In order to measure the muscle quality (MQ), we employ the non-voluntary muscle contraction signal, which occurs by the ES. However, if patient movement, such as voluntary muscle contractionm, occurs during the ES, the electromyography (EMG) sensor captures the VNV muscle contraction signals. In addition, the voluntary muscle contraction signal is a noise component in the MQ measurement technique, which uses only non-voluntary muscle contraction signals. For this reason, we need the VNV muscle contraction classification algorithm to classify the mixed EMG signal. In addition, when recording EMG while using the ES, the EMG signal is significantly contaminated due to the ES signal. Therefore, after we suppress the artifact noise, which is contained in the EMG signal, we perform VNV muscle contraction classification. For this, we first eliminate the artifact noise signal using the ES suppression algorithm. Then, we extract the feature vector, and then the feature vector is reconstructed through the feature selection process. Finally, we design the LSTM-based classification model and compare the proposed algorithm with the conventional method using the EMG data. In addition, to verify the performance of the proposed algorithm, we quantitatively compared results in terms of the confusion matrix and total accuracy. As a result, the performance of the proposed algorithm was higher than that of the conventional methods, including the support vector machine (SVM), artificial neural network (ANN), and deep neural network (DNN).

WR-Hand

This paper presents WR-Hand, a wearable-based system tracking 3D hand pose of 14 hand skeleton points over time using Electromyography (EMG) and gyroscope sensor data from commercial armband. This system provides a significant leap in wearable sensing and enables new application potentials in medical care, human-computer interaction, etc. A challenge is the armband EMG sensors inevitably collect mixed EMG signals from multiple forearm muscles because of the fixed sensor positions on the device, while prior bio-medical models for hand pose tracking are built on isolated EMG signal inputs from isolated forearm spots for different muscles. In this paper, we leverage the recent success of neural networks to enhance the existing bio-medical model using the armband's EMG data and visualize our design to understand why our solution is effective. Moreover, we propose solutions to place the constructed hand pose reliably in a global coordinate system, and address two practical issues by providing a general plug-and-play version for new users without training and compensating for the position difference in how users wear their armbands. We implement a prototype using different commercial armbands, which is lightweight to execute on user's phone in real-time. Extensive evaluation shows the efficacy of the WR-Hand design.

Gold and silver nanocomposite-based biostable and biocompatible electronic textile for wearable electromyographic biosensors

Wearable biosensors have received significant attention due to the possibility of measuring physiological signals on demand. Particularly, the monitoring of electromyographic (EMG) signals on demand by wearable platforms has significant potential to revolutionize the diagnostics and treatment of neuromuscular diseases and for advancing human–computer interfaces. Electronic textile-based biosensors have several advantages, including the simple scale-up process and the ease of fabricating multiple large area electrodes over the whole body to obtain precise measurements. Hence, the electronic textile production requires an affordable approach to fabricate biocompatible and biostable electronic circuits on textile materials. This work explores the possibility of combining screen printing and electrodeposition techniques to produce a biostable nanocomposite-based EMG biosensor on textile. Screen printing was selected to fabricate conductive fabrics that would ultimately be a highly durable textile-based sensor. Silver paste, including microscale silver flakes, was printed on PET/cotton blended fabrics. However, the microscale silver surface was limited for EMG sensors due to low surface area and toxicity, causing low signal detection performance and skin irritation. Gold nanoparticles (Au NPs) were deposited on silver flakes to address the requirements of high-performance and biocompatible biosensors. We confirmed that the gold functionalization improved electrical and electrochemical performance. In addition, various tests were performed to determine electrochemical and biological stability under physiological conditions. The test results proved that Au NPs have successfully encapsulated the surface of silver flakes, preventing the exposure of the silver to the physiological environment. EMG signal recording was performed to confirm the functionalization effect that improved the signal to noise ratio (SNR) of 12.5 with 120 nm Au NPs. Moreover, EMG sensing from bicep workouts and finger movements showed the high sensitivity of the electronic fabrics. Although the SNR of EMG signals dropped to 7.2 after a 15-time washing test, the stabilized SNR after 5 washing cycles indicated that the Au/Ag biosensors showed washing durability. The study demonstrates that this affordable approach can be considered for large-scale production of wearable EMG biosensors.

A soft neuroprosthetic hand providing simultaneous myoelectric control and tactile feedback.

Neuroprosthetic hands are typically heavy (over 400 g) and expensive (more than US$10,000), and lack the compliance and tactile feedback of human hands. Here, we report the design, fabrication and performance of a soft, low-cost and lightweight (292 g) neuroprosthetic hand that provides simultaneous myoelectric control and tactile feedback. The neuroprosthesis has six active degrees of freedom under pneumatic actuation, can be controlled through the input from four electromyography sensors that measure surface signals from residual forearm muscles, and integrates five elastomeric capacitive sensors on the fingertips to measure touch pressure so as to enable tactile feedback by eliciting electrical stimulation on the skin of the residual limb. In a set of standardized tests performed by two individuals with transradial amputations, we show that the soft neuroprosthetic hand outperforms a conventional rigid neuroprosthetic hand in speed and dexterity. We also show that one individual with a transradial amputation wearing the soft neuroprosthetic hand can regain primitive touch sensation and real-time closed-loop control.