Control of an optimal finger exoskeleton based on continuous joint angle estimation from EMG signals (original) (raw)
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Patients suffering from loss of hand functions caused by stroke and other spinal cord injuries have driven a surge in the development of wearable assistive devices in recent years. In this paper, we present a system made up of a low-profile, optimally designed finger exoskeleton continuously controlled by a user's surface electromyographic (sEMG) signals. The mechanical design is based on an optimal four-bar linkage that can model the finger's irregular trajectory due to the finger's varying lengths and changing instantaneous center. The desired joint angle positions are given by the predictive output of an artificial neural network with an EMG-to-Muscle Activation model that parameterizes electromechanical delay (EMD). After confirming good prediction accuracy of multiple finger joint angles we evaluated an index finger exoskeleton by obtaining a subject's EMG signals from the left forearm and using the signal to actuate a finger on the right hand with the exoskeleton. Our results show that our sEMG-based control strategy worked well in controlling the exoskeleton, obtaining the intended positions of the device, and that the subject felt the appropriate motion support from the device.
Improvement of hand functions of SCI patients with EMG-driven hand exoskeleton: a feasibility study
2020
We have developed a one-of-a-kind hand exoskeleton, called Maestro, which can power finger movements of those surviving severe disabilities to complete daily tasks using compliant joints. In this paper, we present results from a practical EMG control strategy conducted with spinal cord injury (SCI) patients (C5, C6 and C7) in which the subjects completed daily tasks controlling Maestro with electromyography (EMG) signals from their forearm muscles. With its compliant actuation and its degrees of freedom (DOF) that match the natural finger movements, Maestro is capable of helping the subjects grasp and manipulate a variety of daily objects (more than 15 from a standardized set). To generate control commands for Maestro, an artificial neural network algorithm was implemented along with a probabilistic control approach to classify and deliver four hand poses robustly with three EMG signals measured from the forearm and palm. Increase in the scores of a standardized test, called the Sol...
Surface electromyography (sEMG) signals are often used in many robot and rehabilitation applications because these reflect the motor intention of users. However, inherent problems such as electromechanical delay are present in such applications. Here, we present a method to estimate finger joint angles using a neural network with inputs obtained from an EMG-to-Activation model which parameterizes this delay. Our results show overall root-mean-square errors of 5-12% between the predicted and actual joint angles. We also show results when the proposed muscle activation input is used compared to using features used by other related studies. Finally, we compare the use of a neural network to a Gaussian Process, which is a popular nonparametric Bayesian regressor that could efficiently give better prediction in this setting.
EMG-driven hand model based on the classification of individual finger movements
Biomedical Signal Processing and Control, 2020
The recovery of hand motion is one of the most challenging aspects in stroke rehabilitation. This paper presents an initial approach to robot-assisted hand-motion therapies. Our goal was twofold: firstly, we have applied machine learning methods to identify and characterize finger motion patterns from healthy individuals. To this purpose, Electromyographic (EMG) signals have been acquired from flexor and extensor muscles in the forearm using surface electrodes. Time and frequency features were used as inputs to machine learning algorithms for recognition of six hand gestures. In particular, we compared the performance of Artificial Neural Networks (ANN), Support Vector Machines (SVM) and k-Nearest Neighbor (k-NN) algorithms for classification. Secondly, each identified gesture was turned into a joint reference trajectory by applying interpolation methods. This allowed us to reconstruct the hand/finger motion kinematics and to simulate the dynamics of each motion pattern. Experiments were carried out to create an EMG database from 20 control subjects, and a VICON camera tracking system was used to validate the accuracy of the proposed system. The average correlation between the EMG-based generated joint trajectories and the tracked hand-motion was 0.91. Furthermore, statistical analysis applied to 14 different SVM, ANN and k-NN configurations showed that Fine k-NN and Weighted k-NN have a better performance for the classification of gestures (98% of accuracy). In a future, the trajectories controlled by EMG signals could be applied to an exoskeleton or hand-robotic prosthesis for rehabilitation.
Continuous Estimation of Finger Joint Angles using Muscle Activation Inputs from Surface EMG Signals
Prediction of dynamic hand finger movements has many clinical and engineering applications in the control of human interface devices such as those used in virtual reality control, robot prosthesis and rehabilitation aids. Surface electromyography (sEMG) signals have often been used in the mentioned applications because these reflect the motor intention of users very well. In this study, we present a method to estimate the finger joint angles of a hand from sEMG signals that considers electromechanical delay (EMD), which is inherent when EMG signals are captured alongside motion data. We use the muscle activation obtained from the sEMG signals as input to a neural network. In this muscle activation model, the EMD is parameterized and automatically obtained through optimization. With this method, we can predict the finger joint angles with sEMG signals in both periodic and nonperiodic free movements of the flexion and extension movement of the fingers. Our results show correlation as high as 0.92 between the actual and predicted metacarpophalangeal (MCP) joint angles for periodic finger flexion movements, and as high as 0.85 for nonperiodic movements, which are more dynamic and natural.
IEEE Access, 2021
Assisted bilateral rehabilitation has been proven to help patients improve their paretic limb ability and promote motor recovery, especially in upper limbs, after suffering a cerebrovascular accident (ACV). Robotic-assisted bilateral rehabilitation based on sEMG-driven control has been previously addressed in other studies to improve hand mobility; however, low-cost embedded solutions for the real-time bio-cooperative control of robotic rehabilitation platforms are lacking. This paper presents the RobHand (Robot for Hand Rehabilitation) system, which is an exoskeleton that supports EMG-driven assisted bilateral by using a custom-made low-cost EMG real-time embedded solution. A threshold non-pattern recognition EMG-driven control for RobHand has been developed, and it detects hand gestures of the healthy hand and replicates the gesture on the exoskeleton placed on the paretic hand. A preliminary study with ten healthy subjects is conducted to evaluate the performance in reliability, tracking accuracy and response time of the proposed EMG-driven control strategy using the EMG real-time embedded solution, and the findings could be extrapolated to stroke patients. A systematic review has been carried out to compare the results of the study, which present a 97% of overall accuracy for the detection of hand gestures and indicate the adequate time responsiveness of the system.
Feasibility of EMG-Based Neural Network Controller for an Upper Extremity Neuroprosthesis
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000
The overarching goal of this project is to provide shoulder and elbow function to individuals with C5/C6 spinal cord injury (SCI) using functional electrical stimulation (FES), increasing the functional outcomes currently provided by a hand neuroprosthesis. The specific goal of this study was to design a controller based on an artificial neural network (ANN) that extracts information from the activity of muscles that remain under voluntary control sufficient to predict appropriate stimulation levels for several paralyzed muscles in the upper extremity. The ANN was trained with activation data obtained from simulations using a musculoskeletal model of the arm that was modified to reflect C5 SCI and FES capabilities. Several arm movements were recorded from able-bodied subjects and these kinematics served as the inputs to inverse dynamic simulations that predicted muscle activation patterns corresponding to the movements recorded. A system identification procedure was used to identify an optimal reduced set of voluntary input muscles from the larger set that are typically under voluntary control in C5 SCI. These voluntary activations were used as the inputs to the ANN and muscles that are typically paralyzed in C5 SCI were the outputs to be predicted. The neural network controller was able to predict the needed FES paralyzed muscle activations from "voluntary" activations with less than a 3.6% RMS prediction error.
An EMG-Controlled Exoskeleton for Hand Rehabilitation
9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005., 2005
The principal goal of this work is the development, testing and experimentation of a device for the hand rehabilitation. The system we designed is intended for people who have partially lost the ability to control correctly the hand musculature, for example after a stroke or a spinal cord injure. Based on EMG signals the system can "understand" the subject volition to move the hand and thanks to its actuators can help the fingers movement in order to perform the task. In this paper we describe the device and discuss the first results conducted on a healthy volunteer.
EMG Signal Processing for Hand Motion Pattern Recognition Using Machine Algorithms
2020
Stroke is a major cause of death and disability in the world. There were approximately 25.7 million stroke survivors and 6.5 million deaths from stroke [1]. Stroke can result in arm disability and reduce daily life activity via weak arm muscle activity [2]. Studies have been performed to discover therapeutic and assistive approaches to compensate for disabilities and restore functions. The emerging rehabilitative robotic systems, including assistive exoskeletons, provide a promising approach to conventional therapy [3-5]. Electromyography signal processing for assistive robot motion control is one of the innovative approaches. Robotic assistive systems have been reported to be able to restore patients’ motor function associated with the neuroplasticity of the brain [6, 7]. In addition, therapy time and quality of therapy are the key factors for functional recovery and improvement [8]. Rehabilitative robots provide significant advantages over classical stroke therapy [9,10]. Robotic ...
Dynamic Modelling of Hand Grasping and Wrist Exoskeleton: An EMG-based Approach
International Journal of Advanced Computer Science and Applications
Human motion intention plays an important role in designing an exoskeleton hand wrist control for post-stroke survivors especially for hand grasping movement. The challenges occurred as sEMG signal frequently being affected by noises from its surroundings. To overcome these issues, this paper aims to establish the relationship between sEMG signal with wrist angle and handgrip force. ANN and ANFIS were two approaches that have been used to design dynamic modelling for hand grasping of wrist movement at different MVC levels. Input sEMG signals value from FDS and EDC muscles were used to predict the hand grip force as a representation of output signal. From the experimental results, sEMG MVC signal level was directly proportional to the hand grip force production while hand grip force signal values will depend on the position of wrist angle. It's also concluded that the hand grip force signal production is higher while the wrist at flexion position compared to extension. A strong relationship between sEMG signal and wrist angle improved the estimation of hand grip force result thus improved the myoelectronic control device for exoskeleton hand. Moreover, ANN managed to improve the estimation accuracy result provided by ANFIS by 0.22% summation of integral absolute error value with similar testing dataset from the experiment.