PDMS/CNT electrodes with bioamplifier for practical in-the-ear and conventional biosignal recordings.

Objective.Potential usage of dry electrodes in emerging applications such as wearable devices, flexible tattoo circuits, and stretchable displays requires that, to become practical solutions, issues such as easy fabrication, strong durability, and low-cost materials must be addressed. The objective of this study was to propose soft and dry electrodes developed from polydimethylsiloxane (PDMS) and carbon nanotube (CNT) composites.Approach.The electrodes were connected with both conventional and in-house NTAmp biosignal instruments for comparative studies. The performances of the proposed dry electrodes were evaluated through electromyogram, electrocardiogram, and electroencephalogram measurements.Main results.Results demonstrated that the capability of the PDMS/CNT electrodes to receive biosignals was on par with that of commercial electrodes (adhesive and gold-cup electrodes). Depending on the type of stimuli, a signal-to-noise ratio of 5-10 dB range was achieved.Significance.The results of the study show that the performance of the proposed dry electrode is comparable to that of commercial electrodes, offering possibilities for diverse applications. These applications may include the physical examination of vital medical signs, the control of intelligent devices and robots, and the transmission of signals through flexible materials.

Variability in quantitative outcomes of instrumental swallowing assessments in adults: a scoping review. / Variabilidade dos desfechos quantitativos nas avaliações instrumentais da deglutição em adultos: uma revisão de escopo.

PURPOSE: To map scientific evidence on the variability of quantitative parameters extracted by instrumental swallowing assessment tests in adults, using the coefficient of variation. RESEARCH STRATEGIES: The methodological procedures recommended by the Joanna Briggs Institute and the extension for scoping reviews of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA-ScR) were followed. SELECTION CRITERIA: The search was carried out in the Pubmed/Medline, Lilacs, Cochrane Library, Embase, Web of Science, Scopus and CINAHL databases, as well as in Google Scholar to consult the gray literature. DATA ANALYSIS: Two blind and independent reviewers screened the articles by title and abstract. Subsequently, the articles were read in full and selected according to the eligibility criteria. Data were extracted according to a standardized instrument. RESULTS: 363 studies were found, 13 of which were eligible. Most studies had a sample size of less than 30 participants and were made up of healthy individuals. The instrumental exams used were diverse: videofluoroscopy, electrical impedance tomography, laryngeal sensors, high-resolution manometry and surface electromyography. The studies searched for intra-individual variability and the coefficient of variation ranged from low to high variability, as the instruments, parameters and collection procedures were very heterogeneous and non-standardized. CONCLUSION: Intra-individual variability of the quantitative outcomes of instrumental swallowing assessments in adults ranged from low to high according to the exam, outcome, presence or absence of underlying disease, consistency and volume of the bolus.

OBJETIVO: Mapear as evidências científicas sobre a variabilidade dos parâmetros quantitativos extraídos por exames instrumentais de avaliação da deglutição em adultos, mediante o coeficiente de variação. ESTRATÉGIA DE PESQUISA: Foram seguidos os procedimentos metodológicos recomendados pelo Joanna Briggs Institute e a extensão para revisões de escopo do Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA-ScR). CRITÉRIOS DE SELEçÃO: A busca foi realizada nas bases de dados Pubmed/Medline, Lilacs, Cochrane Library, Embase, Web of Science, Scopus e CINAHL, assim como no Google Scholar para consultar a literatura cinzenta. ANÁLISE DOS DADOS: Dois revisores cegos e independentes fizeram o rastreamento dos artigos por título e resumo. Posteriormente, os artigos foram lidos na íntegra e selecionados de acordo com os critérios de elegibilidade. Os dados foram extraídos de acordo com um instrumento padronizado. RESULTADOS: Foram encontrados 363 estudos, sendo 13 elegíveis. A maioria dos estudos teve amostra menor que 30 participantes e foi composta por indivíduos saudáveis. Os exames instrumentais utilizados foram diversos: videofluoroscopia, tomografia de impedância elétrica, sensores laríngeos, manometria de alta resolução e eletromiografia de superfície. Os estudos investigaram principalmente a variabilidade intraindividual e os valores do coeficiente de variação oscilaram entre baixa e alta variabilidade, pois os instrumentos, parâmetros e procedimentos de coleta foram heterogêneos e não padronizados. CONCLUSÃO: A variabilidade intraindividual dos parâmetros quantitativos da deglutição obtidos por meio de exames instrumentais em adultos oscila entre baixa e alta conforme o exame, parâmetro testado, presença ou não de doença de base, consistência e volume do bolo alimentar.

Wireless high-resolution surface facial electromyography mask for discrimination of standardized facial expressions in healthy adults.

Wired high resolution surface electromyography (sEMG) using gelled electrodes is a standard method for psycho-physiological, neurological and medical research. Despite its widespread use electrode placement is elaborative, time-consuming, and the overall experimental setting is prone to mechanical artifacts and thus offers little flexibility. Wireless and easy-to-apply technologies would facilitate more accessible examination in a realistic setting. To address this, a novel smart skin technology consisting of wireless dry 16-electrodes was tested. The soft electrode arrays were attached to the right hemiface of 37 healthy adult participants (60% female; 20 to 57 years). The participants performed three runs of a standard set of different facial expression exercises. Linear mixed-effects models utilizing the sEMG amplitudes as outcome measure were used to evaluate differences between the facial movement tasks and runs (separately for every task). The smart electrodes showed specific activation patterns for each of the exercises. 82% of the exercises could be differentiated from each other with very high precision when using the average muscle action of all electrodes. The effects were consistent during the 3 runs. Thus, it appears that wireless high-resolution sEMG analysis with smart skin technology successfully discriminates standard facial expressions in research and clinical settings.

Transforming the Anthropomorphic Passive Free-Flow Foot Prosthesis Into a Powered Foot Prosthesis With Intuitive Control and Sensation (Bionic FFF).

INTRODUCTION: Approximately 89% of all service members with amputations do not return to duty. Restoring intuitive neural control with somatosensory sensation is a key to improving the safety and efficacy of prosthetic locomotion. However, natural somatosensory feedback from lower-limb prostheses has not yet been incorporated into any commercial prostheses. MATERIALS AND METHODS: We developed a neuroprosthesis with intuitive bidirectional control and somatosensation and evoking phase-dependent locomotor reflexes, we aspire to significantly improve the prosthetic rehabilitation and long-term functional outcomes of U.S. amputees. We implanted the skin and bone integrated pylon with peripheral neural interface pylon into the cat distal tibia, electromyographic electrodes into the residual gastrocnemius muscle, and nerve cuff electrodes on the distal tibial and sciatic nerves. Results. The bidirectional neural interface that was developed was integrated into the existing passive Free-Flow Foot and Ankle prosthesis, WillowWood, Mount Sterling, OH. The Free-Flow Foot was chosen because it had the highest Index of Anthropomorphicity among lower-limb prostheses and was the first anthropomorphic prosthesis brought to market. Conclusion. The cats walked on a treadmill with no cutaneous feedback from the foot in the control condition and with their residual distal tibial nerve stimulated during the stance phase of walking.

Perspectives on the comparative benefits of body-powered and myoelectric upper limb prostheses.

BACKGROUND: Patient access to body-powered and myoelectric upper limb prostheses in the United States is often restricted by a healthcare system that prioritizes prosthesis prescription based on cost and perceived value. Although this system operates on an underlying assumption that design differences between these prostheses leads to relative advantages and disadvantages of each device, there is limited empirical evidence to support this view. MAIN TEXT: This commentary article will review a series of studies conducted by our research team with the goal of differentiating how prosthesis design might impact user performance on a variety of interrelated domains. Our central hypothesis is that the design and actuation method of body-powered and myoelectric prostheses might affect users' ability to access sensory feedback and account for device properties when planning movements. Accordingly, other domains that depend on these abilities may also be affected. While our work demonstrated some differences in availability of sensory feedback based on prosthesis design, this did not result in consistent differences in prosthesis embodiment, movement accuracy, movement quality, and overall kinematic patterns. CONCLUSION: Collectively, our findings suggest that performance may not necessarily depend on prosthesis design, allowing users to be successful with either device type depending on the circumstances. Prescription practices should rely more on individual needs and preferences than cost or prosthesis design. However, we acknowledge that there remains a dearth of evidence to inform decision-making and that an expanded research focus in this area will be beneficial.

Neuromuscular disease auxiliary diagnosis using a portable magnetomyographic system.

Objective. The measurement of electromyography (EMG) signals with needle electrodes is widely used in clinical settings for diagnosing neuromuscular diseases. Patients experience pain during needle EMG testing. It is significant to develop alternative diagnostic modalities.Approach. This paper proposes a portable magnetomyography (MMG) measurement system for neuromuscular disease auxiliary diagnosis. Firstly, the design and operating principle of the system are introduced. The feasibility of using the system for auxiliary diagnosis of neuromuscular diseases is then studied. The magnetic signals and needle EMG signals of thirty subjects were collected and compared.Main results. It is found that the amplitude of muscle magnetic field signal increases during mild muscle contraction, and the signal magnitudes of the patients are smaller than those of normal subjects. The diseased muscles tested in the experiment can be distinguished from the normal muscles based on the signal amplitude, using a threshold value of 6 pT. The MMG diagnosis results align well with the needle EMG diagnosis. In addition, the MMG measurement indicates that there is a persistence of spontaneous activity in the diseased muscle.Significance.The experimental results demonstrate that it is feasible to auxiliary diagnose neuromuscular diseases using the portable MMG system, which offers the advantages of non-contact and painless measurements. After more in-depth, systematic, and quantitative research, the portable MMG could potentially be used for auxiliary diagnosis of neuromuscular diseases. The clinical trial registration number is ChiCTR2200067116.

Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics.

Dysphagia is more common in conditions such as stroke, Parkinson's disease, and head and neck cancer. This can lead to pneumonia, choking, malnutrition, and dehydration. Currently, the diagnostic gold standard uses radiologic imaging, the videofluoroscopic swallow study (VFSS); however, it is expensive and necessitates specialized facilities and trained personnel. Although several devices attempt to address the limitations, none offer the clinical-grade quality and accuracy of the VFSS. Here, this study reports a wireless multimodal wearable system with machine learning for automatic, accurate clinical assessment of swallowing behavior and diagnosis of silent aspirations from dysphagia patients. The device includes a kirigami-structured electrode that suppresses changes in skin contact impedance caused by movements and a microphone with a gel layer that effectively blocks external noise for measuring high-quality electromyograms and swallowing sounds. The deep learning algorithm offers the classification of swallowing patterns while diagnosing silent aspirations, with an accuracy of 89.47%. The demonstration with post-stroke patients captures the system's significance in measuring multiple physiological signals in real-time for detecting swallowing disorders, validated by comparing them with the VFSS. The multimodal electronics can ensure a promising future for dysphagia healthcare and rehabilitation therapy, providing an accurate, non-invasive alternative for monitoring swallowing and aspiration events.

Design and Test of an Intraoral Electrode Grid for Tongue High-Density Electromyography.

Tongue motor function is crucial in a wide range of basic activities and its impairment affects quality of life. The electrophysiological assessment of the tongue relies primarily on needle electromyography, which is limited by its invasiveness and inability to capture the concurrent activity of the different tongue muscles. This work aimed at developing an intraoral grid for high-density surface electromyography (HDsEMG) to non-invasively map the electrical excitation of tongue muscles. We developed a grid of 4×8 electrodes deposited over an adhesive 8- µ m thick polyurethane membrane. The testing protocol was conducted on 7 healthy participants and included functional tasks (vowels articulation and tongue movements) aimed at activating different regions of the tongue. The electrical stability of contact was assessed by measuring electrode-tongue impedances before and after the tasks. The spatial amplitude distribution of global EMG and single motor unit action potentials (MUAPs) was characterized. Electrode-tongue impedance magnitude showed no significant changes in the pre-post comparison ( 58±46 k Ω vs. 67±58 k Ω at 50Hz). Contact stability was confirmed by the quality of the signals that allowed to quantify spatiotemporal characteristics of muscle activation during the different tasks. The analysis of the spatial distribution of individual MUAPs amplitude showed that they were confined to relatively small areas on the tongue surface (range: 0.5cm2 -3.9cm [Formula: see text]. A variety of different spatiotemporal MUAP patterns, likely due to the presence of different muscle compartments with different fiber orientations, were observed. Our results demonstrate that the developed electrode grid enables HDsEMG acquisition from the tongue during functional tasks, thus opening new possibilities in tongue muscle assessment both at global and single motor unit level.

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.

Flexible Thermoelectric Wearable Architecture for Wireless Continuous Physiological Monitoring.

Continuous monitoring of physiological signals from the human body is critical in health monitoring, disease diagnosis, and therapeutics. Despite the needs, the existing wearable medical devices rely on either bulky wired systems or battery-powered devices needing frequent recharging. Here, we introduce a wearable, self-powered, thermoelectric flexible system architecture for wireless portable monitoring of physiological signals without recharging batteries. This system harvests an exceptionally high open circuit voltage of 175-180 mV from the human body, powering the wireless wearable bioelectronics to detect electrophysiological signals on the skin continuously. The thermoelectric system shows long-term stability in performance for 7 days with stable power management. Integrating screen printing, laser micromachining, and soft packaging technologies enables a multilayered, soft, wearable device to be mounted on any body part. The demonstration of the self-sustainable wearable system for detecting electromyograms and electrocardiograms captures the potential of the platform technology to offer various opportunities for continuous monitoring of biosignals, remote health monitoring, and automated disease diagnosis.

Identifying alterations in hand movement coordination from chronic stroke survivors using a wearable high-density EMG sleeve.

Objective.Non-invasive, high-density electromyography (HD-EMG) has emerged as a useful tool to collect a range of neurophysiological motor information. Recent studies have demonstrated changes in EMG features that occur after stroke, which correlate with functional ability, highlighting their potential use as biomarkers. However, previous studies have largely explored these EMG features in isolation with individual electrodes to assess gross movements, limiting their potential clinical utility. This study aims to predict hand function of stroke survivors by combining interpretable features extracted from a wearable HD-EMG forearm sleeve.Approach.Here, able-bodied (N= 7) and chronic stroke subjects (N= 7) performed 12 functional hand and wrist movements while HD-EMG was recorded using a wearable sleeve. A variety of HD-EMG features, or views, were decomposed to assess alterations in motor coordination.Main Results.Stroke subjects, on average, had higher co-contraction and reduced muscle coupling when attempting to open their hand and actuate their thumb. Additionally, muscle synergies decomposed in the stroke population were relatively preserved, with a large spatial overlap in composition of matched synergies. Alterations in synergy composition demonstrated reduced coupling between digit extensors and muscles that actuate the thumb, as well as an increase in flexor activity in the stroke group. Average synergy activations during movements revealed differences in coordination, highlighting overactivation of antagonist muscles and compensatory strategies. When combining co-contraction and muscle synergy features, the first principal component was strongly correlated with upper-extremity Fugl Meyer hand sub-score of stroke participants (R2= 0.86). Principal component embeddings of individual features revealed interpretable measures of motor coordination and muscle coupling alterations.Significance.These results demonstrate the feasibility of predicting motor function through features decomposed from a wearable HD-EMG sleeve, which could be leveraged to improve stroke research and clinical care.

Novel Wearable System to Recognize Sign Language in Real Time.

The aim of this study is to develop a practical software solution for real-time recognition of sign language words using two arms. This will facilitate communication between hearing-impaired individuals and those who can hear. We are aware of several sign language recognition systems developed using different technologies, including cameras, armbands, and gloves. However, the system we propose in this study stands out for its practicality, utilizing surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. We address the drawbacks of other methods, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have limited their practical application. Our software can run on different operating systems using digital signal processing and machine learning methods specific to this study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The random forest algorithm emerged as the most successful, achieving a remarkable 99.875% accuracy, while the naïve Bayes algorithm had the lowest success rate with 87.625% accuracy. The new system promises to significantly improve communication for people with hearing disabilities and ensures seamless integration into daily life without compromising user comfort or lifestyle quality.

Microneedle Array Electrodes Fabricated With 3D Printing Technology for High-Quality Electrophysiological Acquisition.

Electrophysiological recordings are vital in assessing biological functions, diagnosing diseases, and facilitating biofeedback and rehabilitation. The applications of conventional wet (gel) electrodes often come with some limitations. Microneedle array electrodes (MAEs) present a possible solution for high-quality electrophysiological acquisition, while the prior technologies for MAE fabrication have been either complex, expensive, or incapable of producing microneedles with uniform dimensions. This work employed a projection stereolithography (P µ SL) 3D printing technology to fabricate MAEs with micrometer-level precision. The MAEs were compared with gel and flat electrodes on electrode-skin interface impedance (EII) and performances of EMG and ECG acquisition. The experimental results indicate that the P µ SL 3D printing technology contributed to an easy-to-perform and low-cost fabrication approach for MAEs. The developed MAEs exhibited promising EII and enabled a stable EMG and ECG acquisition in different conditions without inducing skin allergies, inflammation, or injuries. This research lies in the development of a type of customizable MAE with considerable biomedical application potentials for ultra-minimally invasive or non-invasive electrophysiological acquisition.

An Embedded Electromyogram Signal Acquisition Device.

In this study, we design an embedded surface EMG acquisition device to conveniently collect human surface EMG signals, pursue more intelligent human-computer interactions in exoskeleton robots, and enable exoskeleton robots to synchronize with or even respond to user actions in advance. The device has the characteristics of low cost, miniaturization, and strong compatibility, and it can acquire eight-channel surface EMG signals in real time while retaining the possibility of expanding the channel. This paper introduces the design and function of the embedded EMG acquisition device in detail, which includes the use of wired transmission to adapt to complex electromagnetic environments, light signals to indicate signal strength, and an embedded processing chip to reduce signal noise and perform filtering. The test results show that the device can effectively collect the original EMG signal, which provides a scheme for improving the level of human-computer interactions and enhancing the robustness and intelligence of exoskeleton equipment. The development of this device provides a new possibility for the intellectualization of exoskeleton systems and reductions in their cost.

Design, Fabrication, and Evaluation of 3D Biopotential Electrodes and Intelligent Garment System for Sports Monitoring.

This study presents the development and evaluation of an innovative intelligent garment system, incorporating 3D knitted silver biopotential electrodes, designed for long-term sports monitoring. By integrating advanced textile engineering with wearable monitoring technologies, we introduce a novel approach to real-time physiological signal acquisition, focusing on enhancing athletic performance analysis and fatigue detection. Utilizing low-resistance silver fibers, our electrodes demonstrate significantly reduced skin-to-electrode impedance, facilitating improved signal quality and reliability, especially during physical activities. The garment system, embedded with these electrodes, offers a non-invasive, comfortable solution for continuous ECG and EMG monitoring, addressing the limitations of traditional Ag/AgCl electrodes, such as skin irritation and signal degradation over time. Through various experimentation, including impedance measurements and biosignal acquisition during cycling activities, we validate the system's effectiveness in capturing high-quality physiological data. Our findings illustrate the electrodes' superior performance in both dry and wet conditions. This study not only advances the field of intelligent garments and biopotential monitoring, but also provides valuable insights for the application of intelligent sports wearables in the future.

Wireless Mouth Motion Recognition System Based on EEG-EMG Sensors for Severe Speech Impairments.

This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)-electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for detecting mouth movement based on a new signal processing technology suitable for sensor integration and machine learning applications. This paper examines the relationship between the mouth motion and the brainwave in an effort to develop nonverbal interfacing for people who have lost the ability to communicate, such as people with paralysis. A set of experiments were conducted to assess the efficacy of the proposed method for feature selection. It was determined that the classification of mouth movements was meaningful. EEG-EMG signals were also collected during silent mouthing of phonemes. A few-shot neural network was trained to classify the phonemes from the EEG-EMG signals, yielding classification accuracy of 95%. This technique in data collection and processing bioelectrical signals for phoneme recognition proves a promising avenue for future communication aids.

Potential of a New, Flexible Electrode sEMG System in Detecting Electromyographic Activation in Low Back Muscles during Clinical Tests: A Pilot Study on Wearables for Pain Management.

BACKGROUND: While low back pain (LBP) is the leading cause of disability worldwide, its clinical objective assessment is currently limited. Part of this syndrome arises from the abnormal sensorimotor control of back muscles, involving increased muscle fatigability (i.e., assessed with the Biering-Sorensen test) and abnormal muscle activation patterns (i.e., the flexion-extension test). Surface electromyography (sEMG) provides objective measures of muscle fatigue development (median frequency drop, MDF) and activation patterns (RMS amplitude change). This study therefore assessed the sensitivity and validity of a novel and flexible sEMG system (NSS) based on PEVA electrodes and potentially embeddable in textiles, as a tool for objective clinical LBP assessment. METHODS: Twelve participants wearing NSS and a commercial laboratory sEMG system (CSS) performed two clinical tests used in LBP assessment (Biering-Sorensen and flexion-extension). Erector spinae muscle activity was recorded at T12-L1 and L4-L5. RESULTS: NSS showed sensitivity to sEMG changes associated with fatigue development and muscle activations during flexion-extension movements (p < 0.05) that were similar to CSS (p > 0.05). Raw signals showed moderate cross-correlations (MDF: 0.60-0.68; RMS: 0.53-0.62). Adding conductive gel to the PEVA electrodes did not influence sEMG signal interpretation (p > 0.05). CONCLUSIONS: This novel sEMG system is promising for assessing electrophysiological indicators of LBP during clinical tests.

A portable inflatable soft wearable robot to assist the shoulder during industrial work.

Repetitive overhead tasks during factory work can cause shoulder injuries resulting in impaired health and productivity loss. Soft wearable upper extremity robots have the potential to be effective injury prevention tools with minimal restrictions using soft materials and active controls. We present the design and evaluation of a portable inflatable shoulder wearable robot for assisting industrial workers during shoulder-elevated tasks. The robot is worn like a shirt with integrated textile pneumatic actuators, inertial measurement units, and a portable actuation unit. It can provide up to 6.6 newton-meters of torque to support the shoulder and cycle assistance on and off at six times per minute. From human participant evaluations during simulated industrial tasks, the robot reduced agonist muscle activities (anterior, middle, and posterior deltoids and biceps brachii) by up to 40% with slight changes in joint angles of less than 7% range of motion while not increasing antagonistic muscle activity (latissimus dorsi) in current sample size. Comparison of controller parameters further highlighted that higher assistance magnitude and earlier assistance timing resulted in statistically significant muscle activity reductions. During a task circuit with dynamic transitions among the tasks, the kinematics-based controller of the robot showed robustness to misinflations (96% true negative rate and 91% true positive rate), indicating minimal disturbances to the user when assistance was not required. A preliminary evaluation of a pressure modulation profile also highlighted a trade-off between user perception and hardware demands. Finally, five automotive factory workers used the robot in a pilot manufacturing area and provided feedback.

A Laguerre-Volterra network model based on ant colony optimization applied to evaluate EMG-force relationship in the muscle fatigue state.

With the accuracy and convenience improvement of electromyographic (EMG) acquired by wearable devices, EMG is gradually used to evaluate muscle force signal, a non-invasive evaluation method. However, the relationship between EMG and force is a complex nonlinear relationship, even which will change with different movements and different muscle states. Therefore, it is difficult to evaluate this nonlinear EMG-force relationship, especially when the muscle state gradually transits from non-fatigue to deep fatigue. For more accurate values of force in human fatigue state, this paper proposes a dual-input Laguerre-Volterra network (LVN) model based on ant colony optimization. First, the changes in 19 EMG features are discussed with increasing fatigue. We also consider two non-Gaussian features: kurtosis and negentropy in the 19 features. Later, 11 EMG fatigue features are picked out according to the fatigue test. Then, the preprocessed EMG and a composite signal of the 11 fatigue features are simultaneously input into the LVN model. Subsequently, the ant colony optimization algorithm is selected to train the model parameters. At the same time, a penalty term that we defined is introduced into the model cost function to adjust the weight of each feature adaptively. Finally, some experiments prove that the LVN model could quick fit the accurate force signal in five fatigue stages, such as non-fatigue, slight fatigue, mild fatigue, severe fatigue, and extreme fatigue. This LVN model can quickly transform EMG into strength signal in real time, which is suitable for people to observe muscle strength by a wearable device and makes it easy to detect the muscle current state. This model has good stability and can remain effective for a long time with training once, which provides convenience for the users of wearable devices.

How to use one surface electromyography sensor to recognize six hand movements for a mechanical hand in real time: a method based on Morse code.

Surface electromyography (sEMG) signal is a kind of physiological signal reflecting muscle activity and muscle force. At present, the existing methods of recognizing human motion intention need more than two sensors to recognize more than two kinds of movements, the sensor pasting positions are special, and the hardware conditions for execution are high. In this work, a real-time motion intention recognition method based on Morse code is proposed and applied to the mechanical hand. The short-time and long-term muscle contraction signals collected by a single sEMG sensor were extracted and encoded with the Morse code method, and then the developed mapping method from Morse code to six hand movements were used to recognize hand movements. The average recognition accuracy of hand movements was 94.8704 ± 2.3556%, the average adjusting time was 34.89 s for all subjects, and the execution time of a single movement was 381 ms. The corresponding experiment video can be found in the attachment to show the experiment. The method proposed in this work is a method with one sensor to recognize six movements, low hardware conditions, high recognition accuracy, and insensitive to the difference of sensor pasting position.