Advances and perspectives of mechanomyography

INTRODUCTION: The evaluation of muscular tissue condition can be accomplished with mechanomyography (MMG), a technique that registers intramuscular mechanical waves produced during a fiber's contraction and stretching that are sensed or interfaced on the skin surface. OBJECTIVE: Considering the scope of MMG measurements and recent advances involving the technique, the goal of this paper is to discuss mechanomyography updates and discuss its applications and potential future applications. METHODS: Forty-three MMG studies were published between the years of 1987 and 2013. RESULTS: MMG sensors are developed with different technologies such as condenser microphones, accelerometers, laser-based instruments, etc. Experimental protocols that are described in scientific publications typically investigated the condition of the vastus lateralis muscle and used sensors built with accelerometers, third and fourth order Butterworth filters, 5-100Hz frequency bandpass, signal analysis using Root Mean Square (RMS) (temporal), Median Frequency (MDF) and Mean Power Frequency (MPF) (spectral) features, with epochs of 1 s. CONCLUSION: Mechanomyographic responses obtained in isometric contractions differ from those observed during dynamic contractions in both passive and functional electrical stimulation evoked movements. In the near future, MMG features applied to biofeedback closed-loop systems will help people with disabilities, such as spinal cord injury or limb amputation because they may improve both neural and myoelectric prosthetic control. Muscular tissue assessment is a new application area enabled by MMG; it can be useful in evaluating the muscular tonus in anesthetic blockade or in pathologies such as myotonic dystrophy, chronic obstructive pulmonary disease, and disorders including dysphagia, myalgia and spastic hypertonia. New research becomes necessary to improve the efficiency of MMG systems and increase their application in rehabilitation, clinical and other health areas304384401CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFINANCIADORA DE ESTUDOS E PROJETOS - FINEPsem informaçã

Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation

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

Аналіз методів визначення просторового положення кінцівки пацієнта

У даній статті наведено огляд методів визначення положення верхньої кінцівки людини і їх вплив на класифікацію жестів кисті руки, в результаті якого запропоновано впровадження у вимірювальну систему трьохосьового гіроскопа

Design and Control of a Hand Prosthesis

Práce předkládá metody a výsledky návrhu, výroby a výzkumu pětiprsté protézy ruky. Inspirace jdoucí z přírody a z toho vyvozený princip použitého mechanizmu je uveden. Základní koncept řídícího schéma založeného na procesingu a ohodnocení EMG je navrhnut a implementován. Části senzorického systému protézy jsou navrhnuty a zahrnuty do rídícího algoritmu a shématu. Velké množství inovací a návrhů pro budoucí práce a výzkum jsou prezentovány, stejně tak komplexní analýza a diskuse dosažených a možných budoucích výsledků.The text shows idea flow, methods and results in design, manufacture and research of five--fingered prosthetic hand. The inspiration of the nature and mechanical principle elicited is presented. Fundamental control scheme based on processing and evaluation of EMG is designed and implemented. The segments of sensory system are designed and involved into the overall controll scheme idea. Large innovations and suggestions for future work and research are given with complex discussion through reached and hopefully future results.

The effect of accelerometer mass in mechanomyography measurements

Mechanomyography (MMG) signals record and quantify low-frequency lateral oscillations of active skeletal muscles. These oscillations reflect the ‘‘mechanical counterpart’’ of the motor unit activity measured by electromyography (EMG). Accelerometers have been commonly used to measure MMG. However, the accelerometer mass can affect the MMG signal. The purpose of this paper was to investigate the relationship of the accelerometer mass and the MMG signal. Thirty-two normal volunteers conducted the maximum voluntary contraction of leg extension. MMG signals were obtained from the rectus femoris muscle using an accelerometer. For each subject, the accelerometer mass was varied from 3, 8, 13, 18, 23 and 28 g. The signals were measured for three seconds with a sampling rate of 1kHz. Results showed that the MMG signal amplitude increased as the accelerometer mass increased. However, the median frequency (MF) of the MMG signal decreased with the increased accelerometer mass. When the accelerometer mass increased from 8 g to 13 g, the amplitude of the MMG signal increased the most, and the MF of the MMG signal decreased the most. However, for accelerometers heavier than 13 g, no significant change was observed in both the amplitude and MF. Based on the present study, the mass of the accelerometer is recommended to not exceed 13 g to properly measure MMG signals

Motion Intention Estimation using sEMG-ACC Sensor Fusion

Musculoskeletal injuries can severely impact the ability to produce and control body motion. In order to regain function, rehabilitation is often required. Wearable smart devices are currently under development to provide therapy and assistance for people with impaired arm function. Electromyography (EMG) signals are used as an input to pattern recognition systems to determine intended movements. However, there is a gap between the accuracy of pattern recognition systems in constrained laboratory settings, and usability when used for detecting dynamic unconstrained movements. Motion factors such as limb position, interaction force, and velocity, are known to have a negative impact on the pattern recognition. A possible solution lies in the use of data from other sensors along with the EMG signals, such as signals from accelerometers (ACC), in the training and use of classifiers in order to improve classification accuracy. The objectives of this study were to quantify the impact of motion factors on ACC signals, and to use these ACC signals along with EMG signals for classifying categories of motion factors. To address these objectives, a dataset containing EMG and ACC signals while individuals performed unconstrained arm motions was studied. Analyses of the EMG and accelerometer signals and their use in training classification models to predict characteristics of intended motion were completed. The results quantify how accelerometer features change with variations in arm position, interaction forces, and motion velocities. The results also show that the combination of EMG and ACC data have relatively increased the accuracy of motion intention detection. Velocity could be distinguished between stationary and moving with less than 10% error using a Decision Tree ensemble classifier. Future work should expand on motion factors and EMG-ACC sensor fusion to identify interactions between a person and the environment, in order to guide tuning of control models working towards controlling wearable mechatronic devices during dynamic movements

Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an overview

Human lower limb activity recognition (HLLAR) has grown in popularity over the last decade mainly because to its applications in the identification and control of neuromuscular disorders, security, robotics, and prosthetics. Surface electromyography (sEMG) sensors provide various advantages over other wearable or visual sensors for HLLAR applications, including quick response, pervasiveness, no medical monitoring, and negligible infection. Recognizing lower limb activity from sEMG signals is also challenging owing to the noise in the sEMG signal. Pre- processing of sEMG signals is extremely desirable before the classification because they allow a more consistent and precise evaluation in the above applications. This article provides a segment-by-segment overview of: (1) Techniques for eliminating artifacts from sEMG signals from the lower limb. (2) A survey of existing datasets of lower limb sEMG. (3) A concise description of the various techniques for processing and classifying sEMG data for various applications involving lower limb activity. Finally, an open discussion is presented, which may result in the identification of a variety of future research possibilities for human lower limb activity recognition. Therefore, it is possible to anticipate that the framework presented in this study can aid in the advancement of sEMG-based recognition of human lower limb activity

Time-series analysis based on machine learning for occupational risk evaluation in public administration

Occupational diseases are currently a concerning problem for office workers, who spend long periods of time seated in static positions. Musculoskeletal disorders, specifically, have the highest prevalence among workers, contributing negatively by 17% to the Years Lived with Disability worldwide. This work is part of the PrevOccupAI project, which monitors office workers through wearable sensors and questionnaires, in order to provide them reports that bring to their attention some risk factors that can potentiate occupational diseases. During this work, a study with 40 subjects working in a real environment was carried out. After data pre-processing and synchronization, as it was only intended to analyze sitting data, the periods in which the participants were not seated were removed from the acquired signals. For this purpose, a machine learning model was developed, which uses features from the smartphone’s accelerometer signal to distinguish between sitting and walking. The best model reached an accuracy of 100.0%. Additionally, a model capable of partially predicting the participants’ answers to daily pain questionnaires was developed. Using the electromyography signals and personal information gathered from other questionnaires, it was possible to train a model that predicts if the subject reported pain or not, both at the beginning and end of the working day. Using the Random Forest algorithm, it was possible to achieve a mean accuracy of 86.3%. For each acquisition performed by the 40 participants, a relative ergonomic occupa- tional risk was assigned through variables that characterize postural variability. Using machine learning algorithms, models were trained to attempt to predict the modelled risk. A mean accuracy of 65.7% was achieved for the classification model, and a mean absolute error of 0.84 for the regression model.As doenças ocupacionais são, atualmente, um problema preocupante em trabalhadores de escritório, que passam muito tempo sentados em posições estáticas. As doenças muscu- loesqueléticas, especificamente, são as que têm maior prevalência entre os trabalhadores, contribuindo negativamente em 17% para os Anos Vividos com Incapacidade. Esta dissertação é parte do projeto PrevOccupAI, que monitoriza trabalhadores de escritório através de sensores e questionários, de forma a fornecer-lhes relatórios que cha- mem à sua atenção alguns dos fatores de risco que podem potenciar doenças ocupacionais. Durante este trabalho, foi realizado um estudo em 40 sujeitos a trabalhar em contexto real. Depois de pré-processamento e sincronização dos dados, como só se pretendia analisar dados de trabalhadores sentados, os períodos em que os participantes não estiveram sentados foram retirados dos sinais adquiridos. Para isso, foi desenvolvido um modelo de aprendizagem automática, que usa características do sinal do acelerómetro do telemóvel para distinguir entre sentado e a andar. O melhor modelo atingiu uma exatidão de 100,0%. Adicionalmente, foi desenvolvido um modelo capaz de prever parcialmente as respos- tas dos participantes a questionários diários de dor. Através dos sinais de eletromiografia e informação pessoal retirada de outros questionários, foi possível treinar um modelo que prevê se o sujeito reportou dor ou não, tanto no início como no fim do dia de trabalho. Utilizando o algoritmo de Floresta Aleatória, foi possível atingir uma exatidão média de 86,3%. A cada aquisição realizada pelos 40 participantes foi atribuído um risco ocupacional ergonómico relativo, através de variáveis que caracterizam a variabilidade postural. Uti- lizando algoritmos de aprendizagem automática, foram treinados modelos para tentar prever o risco modelado. Para o modelo de classificação, atingiu-se uma exatidão média de 65,7%, enquanto que para o modelo de regressão se conseguiu que o erro médio absoluto não ultrapassasse 0,84

Surface Electromyography and Artificial Intelligence for Human Activity Recognition - A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation

Human activity recognition (HAR) has become increasingly popular in recent years due to its potential to meet the growing needs of various industries. Electromyography (EMG) is essential in various clinical and biological settings. It is a metric that helps doctors diagnose conditions that affect muscle activation patterns and monitor patients’ progress in rehabilitation, disease diagnosis, motion intention recognition, etc. This review summarizes the various research papers based on HAR with EMG. Over recent years, the integration of Artificial Intelligence (AI) has catalyzed remarkable advancements in the classification of biomedical signals, with a particular focus on EMG data. Firstly, this review meticulously curates a wide array of research papers that have contributed significantly to the evolution of EMG-based activity recognition. By surveying the existing literature, we provide an insightful overview of the key findings and innovations that have propelled this field forward. It explore the various approaches utilized for preprocessing EMG signals, including noise reduction, baseline correction, filtering, and normalization, ensure that the EMG data is suitably prepared for subsequent analysis. In addition, we unravel the multitude of techniques employed to extract meaningful features from raw EMG data, encompassing both time-domain and frequency-domain features. These techniques are fundamental to achieving a comprehensive characterization of muscle activity patterns. Furthermore, we provide an extensive overview of both Machine Learning (ML) and Deep Learning (DL) classification methods, showcasing their respective strengths, limitations, and real-world applications in recognizing diverse human activities from EMG signals. In examining the hardware infrastructure for HAR with EMG, the synergy between hardware and software is underscored as paramount for enabling real-time monitoring. Finally, we also discovered open issues and future research direction that may point to new lines of inquiry for ongoing research toward EMG-based detection.publishedVersio