The Classification of EMG Signals with Zero Retraining in the Influence of User and Rotation Independence

The surface electromyogram (EMG) contains information directly related to muscle contraction and modern classification techniques can obtain near-zero error when identifying various gestures over the forearm. However, good results come at a compromise over the ease of use. Once the EMG classifier trained on a user is changed, the accuracy rate will be greatly reduced. Furthermore, changing the position of the forearm also causes drop in accuracy rate. Acknowledging the limitations of EMG classification, this study aims to investigate the EMG signals based on the gestures, and evaluate if there are any gestures which are inherently robust to these variations. The EMG of forearm gestures have been classified in the combined influence user independence, rotation independence and hand exchange independence. Experiment results on 20 participants indicated that truly independent classification can be achieved for most forearm gestures (up to 100%) in some arm positions. Hand exchange is also not feasible as the study has shown that the data field for both hands are fairly different. Out of the nine gestures under study, only the wrist extension was found to be truly independent of all the influences

Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses

Abstract Background For high usability, myo-controlled devices require robust classification schemes during dynamic contractions. Therefore, this study investigates the impact of the training data set in the performance of several pattern recognition algorithms during dynamic contractions. Methods A 9 class experiment was designed involving both static and dynamic situations. The performance of various feature extraction methods and classifiers was evaluated in terms of classification accuracy. Results It is shown that, combined with a threshold to detect the onset of the contraction, current pattern recognition algorithms used on static conditions provide relatively high classification accuracy also on dynamic situations. Moreover, the performance of the pattern recognition algorithms tested significantly improved by optimizing the choice of the training set. Finally, the results also showed that rather simple approaches for classification of time domain features provide results comparable to more complex classification methods of wavelet features. Conclusions Non-stationary surface EMG signals recorded during dynamic contractions can be accurately classified for the control of multi-function prostheses.</p

근전도 신호를 통한 굴신예측을 이용한 1자유도 무릎관절용 외골격 로봇의 안정한 자유운동 구현

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2016. 2. 조규진.Control system design of exoskeleton type robots is basically different to that of conventional or industrial robots which requires robust motion control. Exoskeleton type robots always interact with human during achieving its own goal, so designing control system of exoskeleton type robot must encounter important issues such as ensuring safety, adaptive and robust performance. Exoskeleton type robots can roughly classified into three categories according to its purpose or target performance, assistance, rehabilitation and human-power augmentation. If attention is narrowed on powered-exoskeleton which has at least one actuator, the common requirement of the each types of exoskeleton to achieving its target performance is to make the exoskeleton interacts with human as if the exoskeleton system acts like desired virtual environment. For example, exoskeleton for assistance of raising arm should have environment which push the human arm upward so that it can assist desired raising motion of human arm. The virtual environment which the exoskeleton simulates could be combination of realistic mechanical system, or any virtual characteristics which actually do not exist in real world. This requirement of rendering virtual environment is common issue on haptics technology which has purpose on transparently simulating virtual environment to human operator. Haptic display have a limitation about rendering virtual environment on two extremes, high impedance and low impedance environments. For given haptic device, when haptic display render hard surface or free motion, It is impossible to render complete hard surface or free motion. It is previously investigated by many researchers that its higher and lower boundary can be written in relatively simple formula which includes system characteristics. This range of rendering is important indicator of evaluation of haptic display performance. Exoskeleton type robots have advantage in gathering extra information from biosignal which enable the system to estimate or predict human movement because exoskeleton usually rigidly bind to human. This paper proposes triggering algorithm using surface electromyography(sEMG) signal for improving performance of haptic display during free motion on admittance-controlled 1DOF exoskeleton. Within the framework of haptics, The purpose of this research is expanding lower impedance boundary of virtual environment by implementing triggering algorithm using sEMG signal. For avoiding complexity caused by including human model, this triggering algorithm is driven by simple pattern-recognition of sEMG signal, not by quantitative evaluation of the signal. Two-port absolute stability criteria is considered for designing the exoskeleton control system so that it guarantees stability with arbitrary characteristics of human operator and virtual environment. The limitations of conventional haptic display to implement free motion and the concept of the triggering algorithm is illustrated. The performance of proposed algorithm is presented by simulation results and experimental results.1.Introduction 1 2.Device Design and Properties 4 2.1 System Description. 4 2.2 Dynamic Characteristics Estimation 6 3.Control System Design 7 3.1 Network Model of Haptic Display 7 3.2 Passivity and Llewellyns Stability Criteria 8 3.3 sEMG Prediction Model and Processing. 10 3.4 Design of Control Algorithm using sEMG 11 4.Simulation and Experiment Results 16 4.1 Control Algorithm Validation with Simulation 16 4.2 Control Algorithm Validation with Experiment 20 5.Conclusion 24 Bibliography 27 국문 초록 29Maste

An experimental evaluation of the incidence of fitness-function/search-algorithm combinations on the classification performance of myoelectric control systems with iPCA tuning

BACKGROUND: The information of electromyographic signals can be used by Myoelectric Control Systems (MCSs) to actuate prostheses. These devices allow the performing of movements that cannot be carried out by persons with amputated limbs. The state of the art in the development of MCSs is based on the use of individual principal component analysis (iPCA) as a stage of pre-processing of the classifiers. The iPCA pre-processing implies an optimization stage which has not yet been deeply explored. METHODS: The present study considers two factors in the iPCA stage: namely A (the fitness function), and B (the search algorithm). The A factor comprises two levels, namely A(1) (the classification error) and A(2) (the correlation factor). Otherwise, the B factor has four levels, specifically B(1) (the Sequential Forward Selection, SFS), B(2) (the Sequential Floating Forward Selection, SFFS), B(3) (Artificial Bee Colony, ABC), and B(4) (Particle Swarm Optimization, PSO). This work evaluates the incidence of each one of the eight possible combinations between A and B factors over the classification error of the MCS. RESULTS: A two factor ANOVA was performed on the computed classification errors and determined that: (1) the interactive effects over the classification error are not significative (F(0.01,3,72) = 4.0659 > f( AB ) = 0.09), (2) the levels of factor A have significative effects on the classification error (F(0.02,1,72) = 5.0162 < f( A ) = 6.56), and (3) the levels of factor B over the classification error are not significative (F(0.01,3,72) = 4.0659 > f( B ) = 0.08). CONCLUSIONS: Considering the classification performance we found a superiority of using the factor A(2) in combination with any of the levels of factor B. With respect to the time performance the analysis suggests that the PSO algorithm is at least 14 percent better than its best competitor. The latter behavior has been observed for a particular configuration set of parameters in the search algorithms. Future works will investigate the effect of these parameters in the classification performance, such as length of the reduced size vector, number of particles and bees used during optimal search, the cognitive parameters in the PSO algorithm as well as the limit of cycles to improve a solution in the ABC algorithm

A novel spatial feature for the identification of motor tasks using high-density electromyography

Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications.Peer ReviewedPostprint (published version

Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol:a single-case experimental design

Background Machine-learning-based myocontrol of prosthetic devices suffers from a high rate of abandonment due to dissatisfaction with the training procedure and with the reliability of day-to-day control. Incremental myocontrol is a promising approach as it allows on-demand updating of the system, thus enforcing continuous interaction with the user. Nevertheless, a long-term study assessing the efficacy of incremental myocontrol is still missing, partially due to the lack of an adequate tool to do so. In this work we close this gap and report about a person with upper-limb absence who learned to control a dexterous hand prosthesis using incremental myocontrol through a novel functional assessment protocol called SATMC (Simultaneous Assessment and Training of Myoelectric Control). Methods The participant was fitted with a custom-made prosthetic setup with a controller based on Ridge Regression with Random Fourier Features (RR-RFF), a non-linear, incremental machine learning method, used to build and progressively update the myocontrol system. During a 13-month user study, the participant performed increasingly complex daily-living tasks, requiring fine bimanual coordination and manipulation with a multi-fingered hand prosthesis, in a realistic laboratory setup. The SATMC was used both to compose the tasks and continually assess the participant's progress. Patient satisfaction was measured using Visual Analog Scales. Results Over the course of the study, the participant progressively improved his performance both objectively, e.g., the time required to complete each task became shorter, and subjectively, meaning that his satisfaction improved. The SATMC actively supported the improvement of the participant by progressively increasing the difficulty of the tasks in a structured way. In combination with the incremental RR-RFF allowing for small adjustments when required, the participant was capable of reliably using four actions of the prosthetic hand to perform all required tasks at the end of the study. Conclusions Incremental myocontrol enabled an upper-limb amputee to reliably control a dexterous hand prosthesis while providing a subjectively satisfactory experience. The SATMC can be an effective tool to this aim