Review on EMG Acquisition and Classification Techniques: Towards Zero Retraining in the Influence of User and Arm Position Independence

The surface electromyogram (EMG) is widely studied and applied in machine control. Recent methods of classifying hand gestures reported classification rates of over 95%. However, the majority of the studies made were performed on a single user, focusing solely on the gesture classification. These studies are restrictive in practical sense: either focusing on just gestures, multi-user compatibility, or rotation independence. The variations in EMG signals due to these conditions present a challenge to the practical application of EMG devices, often requiring repetitious training per application. To the best of our knowledge, there is little comprehensive review of works done in EMG classification in the combined influence of user-independence, rotation and hand exchange. Therefore, in this paper we present a review of works related to the practical issues of EMG with a focus on the EMG placement, and recent acquisition and computing techniques to reduce training. First, we provided an overview of existing electrode placement schemes. Secondly, we compared the techniques and results of single-subject against multi-subject, multi-position settings. As a conclusion, the study of EMG classification in this direction is relatively new. However the results are encouraging and strongly indicate that EMG classification in a broad range of people and tolerance towards arm orientation is possible, and can pave way for more flexible EMG devices

STUDY OF HAND GESTURE RECOGNITION AND CLASSIFICATION

To recognize different hand gestures and achieve efficient classification to understand static and dynamic hand movements used for communications.Static and dynamic hand movements are first captured using gesture recognition devices including Kinect device, hand movement sensors, connecting electrodes, and accelerometers. These gestures are processed using hand gesture recognition algorithms such as multivariate fuzzy decision tree, hidden Markov models (HMM), dynamic time warping framework, latent regression forest, support vector machine, and surface electromyogram. Hand movements made by both single and double hands are captured by gesture capture devices with proper illumination conditions. These captured gestures are processed for occlusions and fingers close interactions for identification of right gesture and to classify the gesture and ignore the intermittent gestures. Real-time hand gestures recognition needs robust algorithms like HMM to detect only the intended gesture. Classified gestures are then compared for the effectiveness with training and tested standard datasets like sign language alphabets and KTH datasets. Hand gesture recognition plays a very important role in some of the applications such as sign language recognition, robotics, television control, rehabilitation, and music orchestration

Addressing the challenges posed by human machine interfaces based on force sensitive resistors for powered prostheses

Despite the advancements in the mechatronics aspect of prosthetic devices, prostheses control still lacks an interface that satisfies the needs of the majority of users. The research community has put great effort into the advancements of prostheses control techniques to address users’ needs. However, most of these efforts are focused on the development and assessment of technologies in the controlled environments of laboratories. Such findings do not fully transfer to the daily application of prosthetic systems. The objectives of this thesis focus on factors that affect the use of Force Myography (FMG) controlled prostheses in practical scenarios. The first objective of this thesis assessed the use of FMG as an alternative or synergist Human Machine Interface (HMI) to the more traditional HMI, i.e. surface Electromyography (sEMG). The assessment for this study was conducted in conditions that are relatively close to the real use case of prosthetic applications. The HMI was embedded in the custom prosthetic prototype that was developed for the pilot participant of the study using an off-the-shelf prosthetic end effector. Moreover, prostheses control was assessed as the user moved their limb in a dynamic protocol.The results of the aforementioned study motivated the second objective of this thesis: to investigate the possibility of reducing the complexity of high density FMG systems without sacrificing classification accuracies. This was achieved through a design method that uses a high density FMG apparatus and feature selection to determine the number and location of sensors that can be eliminated without significantly sacrificing the system’s performance. The third objective of this thesis investigated two of the factors that contribute to increased errors in force sensitive resistor (FSR) signals used in FMG controlled prostheses: bending of force sensors and variations in the volume of the residual limb. Two studies were conducted that proposed solutions to mitigate the negative impact of these factors. The incorporation of these solutions into prosthetic devices is discussed in these studies.It was demonstrated that FMG is a promising HMI for prostheses control. The facilitation of pattern recognition with FMG showed potential for intuitive prosthetic control. Moreover, a method for the design of a system that can determine the required number of sensors and their locations on each individual to achieve a simpler system with comparable performance to high density FMG systems was proposed and tested. The effects of the two factors considered in the third objective were determined. It was also demonstrated that the proposed solutions in the studies conducted for this objective can be used to increase the accuracy of signals that are commonly used in FMG controlled prostheses

Machine Learning-Based Hand Gesture Recognition via EMG Data

Electromyography (EMG) data gives information about the electrical activity related to muscles. EMG data obtained from arm through sensors helps to understand hand gestures. For this work, hand gesture data were taken from UCI2019 EMG dataset obtained from MYO thalmic armband were classied with six dierent machine learning algorithms. Articial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF) methods were preferred for comparison based on several performance metrics which are accuracy, precision, sensitivity, specicity, classication error, kappa, root mean squared error (RMSE) and correlation. The data belongs to seven hand gestures. 700 samples from 7 classes (100 samples per group) were used in the experiments. The splitting ratio in the classication was 0.8-0.2, i.e. 80% of the samples were used in training and 20% of data were used in testing phase of the classier. NB was found to be the best among other methods because of high accuracy (96.43%) and sensitivity (96.43%) and the lowest RMSE (0.189). Considering the results of the performance parameters, it can be said that this study recognizes and classies seven hand gestures successfully in comparison with the literature

On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

Recent Advances in Motion Analysis

The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application

Regressing Grasping Using Force Myography: An Exploratory Study

Background: Partial hand amputation forms more than 90% of all upper limb amputations. This amputation has a notable efect on the amputee’s life. To improve the quality of life for partial hand amputees diferent prosthesis options, including externallypowered prosthesis, have been investigated. The focus of this work is to explore force myography (FMG) as a technique for regressing grasping movement accompanied by wrist position variations. This study can lay the groundwork for a future investigation of FMG as a technique for controlling externally-powered prostheses continuously. Methods: Ten able-bodied participants performed three hand movements while their wrist was fxed in one of six predefned positions. The angle between Thumb and Index fnger (θTI), and Thumb and Middle fnger (θTM) were calculated as measures of grasping movements. Two approaches were examined for estimating each angle: (i) one regression model, trained on data from all wrist positions and hand movements; (ii) a classifer that identifed the wrist position followed by a separate regression model for each wrist position. The possibility of training the system using a limited number of wrist positions and testing it on all positions was also investigated. Results: The frst approach had a correlation of determination (R2) of 0.871 for θTI and R2 θTM = 0.941. Using the second approach R2 θTI = 0.874 and R2 θTM = 0.942 were obtained. The frst approach is over two times faster than the second approach while having similar performance; thus the frst approach was selected to investigate the efect of the wrist position variations. Training with 6 or 5 wrist positions yielded results which were not statistically signifcant. A statistically signifcant decrease in performance resulted when less than fve wrist positions were used for training. Conclusions: The results indicate the potential of FMG to regress grasping movement, accompanied by wrist position variations, with a regression model for each angle. Also, it is necessary to include more than one wrist position in the training phase