Robust Electromyography Based Control of Multifunctional Prostheses of The Upper Extremity

Multifunctional, highly dexterous and complex mechanic hand prostheses are emerging and currently entering the market. However, the bottleneck to fully exploiting all capabilities of these mechatronic devices, and to making all available functions controllable reliably and intuitively by the users, remains a considerable challenge. The robustness of scientific methods proposed to overcome this barrier is a crucial factor for their future commercial success. Therefore, in this thesis the matter of robust, multifunctional and dexterous control of prostheses of the upper limb was addressed and some significant advancements in the scientific field were aspired. To this end, several investigations grouped in four studies were conducted, all with the same focus on understanding mechanisms that influence the robustness of myoelectric control and resolving their deteriorating effects. For the first study, a thorough literature review of the field was conducted and it was revealed that many non-stationarities, which could be expected to affect the reliability of surface EMG pattern recognition myoprosthesis control, had been identified and studied previously. However, one significant factor had not been addressed to a sufficient extent: the effect of long-term usage and day-to-day testing. Therefore, a dedicated study was designed and carried out, in order to address the previously unanswered question of how reliable surface electromyography pattern recognition was across days. Eleven subjects, involving both able-bodied and amputees, participated in this study over the course of 5 days, and a pattern recognition system was tested without daily retraining. As the main result of this study, it was revealed that the time between training and testing a classifier was indeed a very relevant factor influencing the classification accuracy. More estimation errors were observed as more time lay between the classifier training and testing. With the insights obtained from the first study, the need for compensating signal non-stationarities was identified. Hence, in a second study, building up on the data obtained from the first investigation, a self-correction mechanism was elaborated. The goal of this approach was to increase the systems robustness towards non-stationarities such as those identified in the first study. The system was capable of detecting and correcting its own mistakes, yielding a better estimation of movements than the uncorrected classification or other, previously proposed strategies for error removal. In the third part of this thesis, the previously investigated ideas for error suppression for increased robustness of a classification based system were extended to regression based movement estimation. While the same method as tested in the second study was not directly applicable to regression, the same underlying idea was used for developing a novel proportional estimator. It was validated in online tests, with the control of physical prostheses by able-bodied and transradial amputee subjects. The proposed method, based on common spatial patterns, outperformed two state-of-the art control methods, demonstrating the benefit of increased robustness in movement estimation during applied tasks. The results showed the superior performance of robust movement estimation in real life investigations, which would have hardly been observable in offline or abstract cursor control tests, underlining the importance of tests with physical prostheses. In the last part of this work, the limitation of sequential movements of the previously explored system was addressed and a methodology for enhancing the system with simultaneous and proportional control was developed. As a result of these efforts, a system robust, natural and fluent in its movements was conceived. Again, online control tests of physical prostheses were performed by able-bodied and amputee subjects, and the novel system proved to outperform the sequential controller of the third study of this thesis, yielding the best control technique tested. An extensive set of tests was conducted with both able-bodied and amputee subjects, in scenarios close to clinical routine. Custom prosthetic sockets were manufactured for all subjects, allowing for experimental control of multifunction prostheses with advanced machine learning based algorithms in real-life scenarios. The tests involved grasping and manipulating objects, in ways as they are often encountered in everyday living. Similar investigations had not been conducted before. One of the main conclusions of this thesis was that the suppression of wrong prosthetic motions was a key factor for robust prosthesis control and that simultaneous wrist control was a beneficial asset especially for experienced users. As a result of all investigations performed, clinically relevant conclusions were drawn from these tests, maximizing the impact of the developed systems on potential future commercialization of the newly conceived control methods. This was emphasized by the close collaboration with Otto Bock as an industrial partner of the AMYO project and hence this work.2016-02-2

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

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

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

Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA Clustering

© 2001-2011 IEEE. Surface electromyography (sEMG)-based pattern recognition studies have been widely used to improve the classification accuracy of upper limb gestures. Information extracted from multiple sensors of the sEMG recording sites can be used as inputs to control powered upper limb prostheses. However, usage of multiple EMG sensors on the prosthetic hand is not practical and makes it difficult for amputees due to electrode shift/movement, and often amputees feel discomfort in wearing sEMG sensor array. Instead, using fewer numbers of sensors would greatly improve the controllability of prosthetic devices and it would add dexterity and flexibility in their operation. In this paper, we propose a novel myoelectric control technique for identification of various gestures using the minimum number of sensors based on independent component analysis (ICA) and Icasso clustering. The proposed method is a model-based approach where a combination of source separation and Icasso clustering was utilized to improve the classification performance of independent finger movements for transradial amputee subjects. Two sEMG sensor combinations were investigated based on the muscle morphology and Icasso clustering and compared to Sequential Forward Selection (SFS) and greedy search algorithm. The performance of the proposed method has been validated with five transradial amputees, which reports a higher classification accuracy (> 95%). The outcome of this study encourages possible extension of the proposed approach to real time prosthetic applications