Estimation and Early Prediction of Grip Force Based on sEMG Signals and Deep Recurrent Neural Networks

Hands are used for communicating with the surrounding environment and have a complex structure that enables them to perform various tasks with their multiple degrees of freedom. Hand amputation can prevent a person from performing their daily activities. In that event, finding a suitable, fast, and reliable alternative for the missing limb can affect the lives of people who suffer from such conditions. As the most important use of the hands is to grasp objects, the purpose of this study is to accurately predict gripping force from surface electromyography (sEMG) signals during a pinch-type grip. In that regard, gripping force and sEMG signals are derived from 10 healthy subjects. Results show that for this task, recurrent networks outperform nonrecurrent ones, such as a fully connected multilayer perceptron (MLP) network. Gated recurrent unit (GRU) and long short-term memory (LSTM) networks can predict the gripping force with R-squared values of 0.994 and 0.992, respectively, and a prediction rate of over 1300 predictions per second. The predominant advantage of using such frameworks is that the gripping force can be predicted straight from preprocessed sEMG signals without any form of feature extraction, not to mention the ability to predict future force values using larger prediction horizons adequately. The methods presented in this study can be used in the myoelectric control of prosthetic hands or robotic grippers.Comment: 9 pages, accepted for publication in journal of the Brazilian Society of Mechanical Sciences and Engineerin

Estimation of EMG-Based force using a neural-network-based approach

© 2013 IEEE. The dynamics of human arms has a high impact on the humans' activities in daily life, especially when a human operates a tool such as interactions with a robot with the need for high dexterity. The dexterity of human arms depends largely on motor functionality of muscle. In this sense, the dynamics of human arms should be well analyzed. In this paper, in order to analyse the characteristic of human arms, a neural-network-based algorithm is proposed for exploring the potential model between electromyography (EMG) signal and human arm's force. Based on the analysis of force for humans, the mean absolute value of the electromyographic signal is selected as the input for the potential model. In this paper, in order to accurately estimate the potential model, three domains fuzzy wavelet neural network (TDFWNN) algorithm without prior knowledge of the biomechanical model is utilized. The performance of the proposed algorithm has been demonstrated by the experimental results in comparison with the conventional radial basis function neural network (RBFNN) method. By comparison, the proposed TDFWNN algorithm provides an effective solution to evaluate the influence of human factors based on biological signals

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

From Unimodal to Multimodal: improving the sEMG-Based Pattern Recognition via deep generative models

Multimodal hand gesture recognition (HGR) systems can achieve higher recognition accuracy. However, acquiring multimodal gesture recognition data typically requires users to wear additional sensors, thereby increasing hardware costs. This paper proposes a novel generative approach to improve Surface Electromyography (sEMG)-based HGR accuracy via virtual Inertial Measurement Unit (IMU) signals. Specifically, we trained a deep generative model based on the intrinsic correlation between forearm sEMG signals and forearm IMU signals to generate virtual forearm IMU signals from the input forearm sEMG signals at first. Subsequently, the sEMG signals and virtual IMU signals were fed into a multimodal Convolutional Neural Network (CNN) model for gesture recognition. To evaluate the performance of the proposed approach, we conducted experiments on 6 databases, including 5 publicly available databases and our collected database comprising 28 subjects performing 38 gestures, containing both sEMG and IMU data. The results show that our proposed approach outperforms the sEMG-based unimodal HGR method (with increases of 2.15%-13.10%). It demonstrates that incorporating virtual IMU signals, generated by deep generative models, can significantly enhance the accuracy of sEMG-based HGR. The proposed approach represents a successful attempt to transition from unimodal HGR to multimodal HGR without additional sensor hardware

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

<p>Abstract</p> <p>Background</p> <p>Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.</p> <p>Methods</p> <p>Both sEMG data from four muscles of the forearm and wrist torque were collected from eight volunteers by using a custom-made testing rig. The features that were extracted from the sEMG signals included root mean square (rms) EMG amplitude, autoregressive (AR) model coefficients and waveform length. Support Vector Machines (SVM) was employed to extract classes of different force intensity from the sEMG signals. After assessing the off-line performance of the used classification technique, the WEP was used to validate in real-time the proposed classification scheme.</p> <p>Results</p> <p>The data gathered from the volunteers were divided into two sets, one with nineteen classes and the second with thirteen classes. Each set of data was further divided into training and testing data. It was observed that the average testing accuracy in the case of nineteen classes was about 88% whereas the average accuracy in the case of thirteen classes reached about 96%. Classification and control algorithm implemented in the WEP was executed in less than 125 ms.</p> <p>Conclusions</p> <p>The results of this study showed that classification of EMG signals by separating different levels of torque is possible for wrist motion and the use of only four EMG channels is suitable. The study also showed that SVM classification technique is suitable for real-time classification of sEMG signals and can be effectively implemented for controlling an exoskeleton device for assisting the wrist.</p

Comparison of regression models for estimation of isometric wrist joint torques using surface electromyography

Background: Several regression models have been proposed for estimation of isometric joint torque using surfaceelectromyography (SEMG) signals. Common issues related to torque estimation models are degradation of modelaccuracy with passage of time, electrode displacement, and alteration of limb posture. This work compares theperformance of the most commonly used regression models under these circumstances, in order to assistresearchers with identifying the most appropriate model for a specific biomedical application.Methods: Eleven healthy volunteers participated in this study. A custom-built rig, equipped with a torque sensor,was used to measure isometric torque as each volunteer flexed and extended his wrist. SEMG signals from eightforearm muscles, in addition to wrist joint torque data were gathered during the experiment. Additional data weregathered one hour and twenty-four hours following the completion of the first data gathering session, for thepurpose of evaluating the effects of passage of time and electrode displacement on accuracy of models. AcquiredSEMG signals were filtered, rectified, normalized and then fed to models for training.Results: It was shown that mean adjusted coefficient of determination (R2a) values decrease between 20%-35% fordifferent models after one hour while altering arm posture decreased mean R2avalues between 64% to 74% fordifferent models.Conclusions: Model estimation accuracy drops significantly with passage of time, electrode displacement, andalteration of limb posture. Therefore model retraining is crucial for preserving estimation accuracy. Data resamplingcan significantly reduce model training time without losing estimation accuracy. Among the models compared,ordinary least squares linear regression model (OLS) was shown to have high isometric torque estimation accuracycombined with very short training times

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

Disturbance observer enhanced variable gain controller for robot teleoperation with motion capture using wearable armbands

Disturbance observer (DOB) based controller performs well in estimating and compensating for perturbation when the external or internal unknown disturbance is slowly time varying. However, to some extent, robot manipulators usually work in complex environment with high-frequency disturbance. Thereby, to enhance tracking performance in a teleoperation system, only traditional DOB technique is insufficient. In this paper, for the purpose of constructing a feasible teleoperation scheme, we develop a novel controller that contains a variable gain scheme to deal with fast-time varying perturbation, whose gain is adjusted linearly according to human surface electromyographic signals collected from Myo wearable armband. In addition, for tracking the motion of operator’s arm, we derive five-joint-angle data of a moving human arm through two groups of quaternions generated from the armbands. Besides, the radial basis function neural networks and the disturbance observer-based control (DOBC) approaches are fused together into the proposed controller to compensate the unknown dynamics uncertainties of the slave robot as well as environmental perturbation. Experiments and simulations are conducted to demonstrated the effectiveness of the proposed strategy