Studies of the relationship between the surface electromyogram, joint torque and impedance

This compendium-format dissertation (i.e., comprised mostly of published and in-process articles) primarily reports on system identification methods that relate the surface electromyogram (EMG)—the electrical activity of skeletal muscles—to mechanical kinetics. The methods focus on activities of the elbow and hand-wrist. The relationship between the surface EMG and joint impedance was initially studied. My work provided a complete second-order EMG-based impedance characterization of stiffness, viscosity and inertia over a complete range of nominal torques, from a single perturbation trial with slowly varied torque. A single perturbation trial provides a more convenient method for impedance evaluation. The RMS errors of the EMG-based method were 20.01% for stiffness and 7.05% for viscosity, compared with the traditional mechanical measurement. Three projects studied the relationship between EMG and force/torque, a topic that has been studied for a number of years. Optimal models use whitened EMG amplitude, combining multiple EMG channels and a polynomial equation to describe this relationship. First, we used three techniques to improve current models at the elbow joint. Three more features were extracted from the EMG (waveform length, slope sign change rate and zero crossing rate), in addition to EMG amplitude. Each EMG channel was used separately, compared to previous studies which combined multiple channels from biceps and, separately, from triceps muscles. Finally, an exponential power law model was used. Each of these improvement techniques showed better performance (P\u3c0.05 and ~0.7 percent maximum voluntary contraction (%MVC) error reduction from a nominal error of 5.5%MVC) than the current “optimal” model. However, the combination of pairs of these techniques did not further improve results. Second, traditional prostheses only control 1 degree of freedom (DoF) at a time. My work provided evidence for the feasibility of controlling 2-DoF wrist movements simultaneously, with a minimum number of electrodes. Results suggested that as few as four conventional electrodes, optimally located about the forearm, could provide 2-DoF simultaneous, independent and proportional control with error ranging from 9.0–10.4 %MVC, which is similar to the 1-DoF approach (error from 8.8–9.8 %MVC) currently used for commercial prosthesis control. The third project was similar to the second, except that this project studied controlling a 1-DoF wrist with one hand DoF simultaneously. It also demonstrated good performance with the error ranging from 7.8-8.7 %MVC, compared with 1-DoF control. Additionally, I participated in two team projects—EMG decomposition and static wrist EMG to torque—which are described herein

Neuro-Musculoskeletal Mapping for Man-Machine Interfacing.

We propose a myoelectric control method based on neural data regression and musculoskeletal modeling. This paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-EMG) decomposition to estimate muscle excitations. The muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on artificial neural networks in two amputees and in four out of six intact-bodied subjects. In addition to joint kinematics, the proposed data-driven model-based approach also estimated several biomechanical variables in a full feed-forward manner that could potentially be useful in supporting the rehabilitation and training process. These results indicate that using a full forward dynamics musculoskeletal model directly driven by motor neuron activity is a promising approach in rehabilitation and prosthetics to model the series of transformations from muscle excitation to resulting joint function

Grasp force estimation from the transient EMG using high-density surface recordings.

Objective: Understanding the neurophysiological signals underlying voluntary motor control and decoding them for prosthesis control are among the major challenges in applied neuroscience and bioengineering. Usually, information from the electrical activity of residual forearm muscles (i.e. the electromyogram, EMG) is used to control different functions of a prosthesis. Noteworthy, forearm EMG patterns at the onset of a contraction (transient phase) have shown to contain predictive information about upcoming grasps. However, decoding this information for the estimation of grasp force was so far overlooked. Approach: High Density-EMG signals (192 channels) were recorded from twelve participants performing a pick-and-lift task. The final grasp force was estimated offline using linear regressors, with four subsets of channels and ten features obtained using three channels-features selection methods. Two different evaluation metrics (absolute error and R2), complemented with statistical analysis, were used to select the optimal configuration of the parameters. Different windows of data starting at the grasp force (GF) onset were compared to determine the time at which the grasp force can be ascertained from the EMG signals. Main results: The prediction accuracy improved by increasing the window length from the moment of the onset and kept improving until the steady state at which a plateau of performances was reached. With our methodology, estimations of the grasp force through 16 EMG channels reached an absolute error of 2.52% the maximum voluntary force using only transient information and 1.99% with the first 500ms of data following the onset. Significance: The final GF estimation from transient EMG was comparable to the one obtained using steady state data, confirming our hypothesis that the transient phase contains information about the final grasp force. This result paves the way to fast online myoelectric controllers capable of decoding grasp strength from the very early portion of the EMG signal

Myoelectric Control Systems for Hand Rehabilitation Device: A Review

One of the challenges of the hand rehabilitation device is to create a smooth interaction between the device and user. The smooth interaction can be achieved by considering myoelectric signal generated by human's muscle. Therefore, the so-called myoelectric control system (MCS) has been developed since the 1940s. Various MCS's has been proposed, developed, tested, and implemented in various hand rehabilitation devices for different purposes. This article presents a review of MCS in the existing hand rehabilitation devices. The MCS can be grouped into main groups, the non-pattern recognition and pattern recognition ones. In term of implementation, it can be classified as MCS for prosthetic and exoskeleton hand. Main challenges for MCS today is the robustness issue that hampers the implementation of MCS on the clinical application

Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing

There are some practical factors, such as arm position change and donning/doffing, which prevent robust myoelectric control. The objective of this study is to precisely characterize the impacts of the two representative factors on myoelectric controllability in practical control situations, thereby providing useful references that can be potentially used to find better solutions for clinically reliable myoelectric control. To this end, a real-time target acquisition task was performed by fourteen subjects including one individual with congenital upper-limb deficiency, where the impacts of arm position change, donning/doffing and a combination of both factors on control performance was systematically evaluated. The changes in online performance were examined with seven different performance metrics to comprehensively evaluate various aspects of myoelectric controllability. As a result, arm position change significantly affects offline prediction accuracy, but not online control performance due to real-time feedback, thereby showing no significant correlation between offline and online performance. Donning/doffing was still problematic in online control conditions. It was further observed that no benefit was attained when using a control model trained with multiple position data in terms of arm position change, and the degree of electrode shift caused by donning/doffing was not severely associated with the degree of performance loss under practical conditions (around 1 cm electrode shift). Since this study is the first to concurrently investigate the impacts of arm position change and donning/doffing in practical myoelectric control situations, all findings of this study provide new insights into robust myoelectric control with respect to arm position change and donning/doffing.DFG, 325093850, Open Access Publizieren 2017 - 2018 / Technische Universität Berli

Estimation of distal arm joint angles from EMG and shoulder orientation for transhumeral prostheses

Prior studies have shown that shoulder orientation and upper-arm electromyography (EMG), taken separately, are predictors of both elbow flexion/extension and forearm pronation/supination during arm movements. In this thesis, we quantify the extent to which shoulder orientation, upper-arm EMG, and forearm EMG are predictors of distal arm joint angles for subjects without impairment, as well as subjects with a unilateral transhumeral amputation and targeted reinnervation. In principle, the results presented provide the basis for choosing inputs for control of transhumeral prostheses, both by subjects with targeted motor reinnervation (when forearm EMG is available) and by subjects without target motor reinnervation (when forearm EMG is not available)

Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography.

The study of hand and finger movement is an important topic with applications in prosthetics, rehabilitation, and ergonomics. Surface electromyography (sEMG) is the gold standard for the analysis of muscle activation. Previous studies investigated the optimal electrode number and positioning on the forearm to obtain information representative of muscle activation and robust to movements. However, the sEMG spatial distribution on the forearm during hand and finger movements and its changes due to different hand positions has never been quantified. The aim of this work is to quantify 1) the spatial localization of surface EMG activity of distinct forearm muscles during dynamic free movements of wrist and single fingers and 2) the effect of hand position on sEMG activity distribution. The subjects performed cyclic dynamic tasks involving the wrist and the fingers. The wrist tasks and the hand opening/closing task were performed with the hand in prone and neutral positions. A sensorized glove was used for kinematics recording. sEMG signals were acquired from the forearm muscles using a grid of 112 electrodes integrated into a stretchable textile sleeve. The areas of sEMG activity have been identified by a segmentation technique after a data dimensionality reduction step based on Non Negative Matrix Factorization applied to the EMG envelopes. The results show that 1) it is possible to identify distinct areas of sEMG activity on the forearm for different fingers; 2) hand position influences sEMG activity level and spatial distribution. This work gives new quantitative information about sEMG activity distribution on the forearm in healthy subjects and provides a basis for future works on the identification of optimal electrode configuration for sEMG based control of prostheses, exoskeletons, or orthoses. An example of use of this information for the optimization of the detection system for the estimation of joint kinematics from sEMG is reported