In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This paper’s hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised 19 and 17 able-bodied participants, respectively (the first one is employed for pre-training), were recorded for this work, using the Myo armband. A third Myo armband dataset was taken from the NinaPro database and is comprised ten able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, spectrograms, and continuous wavelet transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.

/pdf/deep-learning-for-electromyographic-hand-gesture-signal-35yh5gi4gl.pdf

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

A fundamental component of many modern prostheses is the myoelectric control system, which uses the electromyogram (EMG) signals from an individual's muscles to control the prosthesis movements. Despite the extensive research focus on the myoelectric control of arm and gross hand movements, more dexterous individual and combined fingers control has not received the same attention. The main contribution of this paper is an investigation into accurately discriminating between individual and combined fingers movements using surface EMG signals, so that different finger postures of a prosthetic hand can be controlled in response. For this purpose, two EMG electrodes located on the human forearm are utilized to collect the EMG data from eight participants. Various feature sets are extracted and projected in a manner that ensures maximum separation between the finger movements and then fed to two different classifiers. The second contribution is the use of a Bayesian data fusion postprocessing approach to maximize the probability of correct classification of the EMG data belonging to different movements. Practical results and statistical significance tests prove the feasibility of the proposed approach with an average classification accuracy of ~90% across different subjects proving the significance of the proposed fusion scheme in finger movement classification.

Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals

Myoelectric signals (MES) have been used in various applications, in particular, for identification of user intention to potentially control assistive devices for amputees, orthotic devices, and exoskeleton in order to augment capability of the user. MES are also used to estimate force and, hence, torque to actuate the assistive device. The application of MES is not limited to assistive devices, and they also find potential applications in teleoperation of robots, haptic devices, virtual reality, and so on. The myoelectric control-based prosthetic hand aids to restore activities of daily living of amputees in order to improve the self-esteem of the user. All myoelectric control-based prosthetic hands may not have similar operations and exhibit variation in sensing input, deciphering the signals, and actuating prosthetic hand. Researchers are focusing on improving the functionality of prosthetic hand in order to suit the user requirement with the different operating features. The myoelectric control differs in operation to accommodate various external factors. This article reviews the state of the art of myoelectric prosthetic hand, giving description of each control strategy.

/pdf/myoelectric-control-of-prosthetic-hands-state-of-the-art-1kz2u436b1.pdf

Myoelectric control of prosthetic hands: state-of-the-art review.

Control algorithms for upper limb myoelectric prostheses have been in development since the mid-1940s. Despite advances in computing power and in the performance of these algorithms, clinically available prostheses are still based on the earliest control strategies. The aim of this review paper is to detail the development, advantages and disadvantages of prosthetic control systems and to highlight areas that are current barriers for the transition from laboratory to clinical practice. Current surgical strategies and future research directions to achieve multifunctional control will also be discussed. The findings from this review suggest that regression algorithms may offer an alternative feed-forward approach to direct and pattern recognition control, while virtual rehabilitation environments and tactile feedback could improve the overall prosthetic control.

https://link.springer.com/content/pdf/10.1007%2Fs40137-013-0044-8.pdf

Prosthetic Myoelectric Control Strategies: A Clinical Perspective

With the ever increasing role of computerized machines in society, Human Computer Interaction (HCI) system has become an increasingly important part of our daily lives. HCI determines the effective utilization of the available information flow of the computing, communication, and display technologies. In recent years, there has been a
tremendous interest in introducing intuitive interfaces that can recognize the user's body movements and translate them into machine commands. For the neural linkage with
computers, various biomedical signals (biosignals) can be used, which can be acquired from a specialized tissue, organ, or cell system like the nervous system. Examples include Electro-Encephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG). Such approaches are extremely valuable to physically disabled persons. Many attempts have been made to use EMG signal from gesture for developing HCI. EMG signal processing and controller work is currently proceeding in various direction including the
development of continuous EMG signal classification for graphical controller, that enables the physically disabled to use word processing programs and other personal computer
software, internet. It also enable manipulation of robotic devices, prosthesis limb, I/O for virtual reality games, physical exercise equipments etc. Most of the developmental area is based on pattern recognition using neural networks. The EMG controller can be programmed to perform gesture recognition based on signal analysis of groups of muscles
action potential. This review paper is to discuss the various methodologies and algorithms used for EMG signal classification for the purpose of interpreting the EMG signal into computer command.

EMG signal classification for human computer interaction: a review

Myoelectric signals (MES) are the electrical manifestation of muscular contractions and they can be used to create myoelectric prosthesis which are able to function with the amputee's muscle movements. This signal recorded at the surface of the skin of the forearm has been exploited to provide recognition of the movement of the hand and finger Movements of healthy subject. The objective of the paper is to describe the identification procedure, based on EMG patterns of forearm activity using various Neural Networks methods and to make a comparison between different intelligent computational methods of identification, which are used in this work. Then an online algorithm for movement identification and classification that utilises the trained Neural Networks is presented.

Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals

This work is an attempt to illustrate the utility and effectiveness of soft-computing approaches in handling the modelling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, and evolutionary algorithms) in a complementary hybrid framework for solving real world problems. The present work concentrates on the pioneering neuro-fuzzy system ANFIS (adaptive neuro fuzzy inference system). ANFIS is first used to model nonlinear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behaviour of the underlying system and for the design and evaluation of various intelligent control strategies.

ANFIS based modelling and control of non-linear systems : a tutorial

This work is an attempt to illustrate the utility and effectiveness of soft computing approaches in handling the modeling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, evolutionary algorithms, ...) in a complementary hybrid framework for solving real world problems. There are several approaches to integrate neural networks and fuzzy logic to form a neuro-fuzzy system. The present work will concentrate on the pioneering neuro-fuzzy system, Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is first used to model non-linear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behavior of the underlying system and for the design and evaluation of various intelligent control strategies.

Modeling and control of non-linear systems using soft computing techniques

Mouloud Denai, Frank Palis, Abdelhafid Zeghbib, ‘ANFIS Based Modeling and Control of Nonlinear Systems: A Tutorial’, paper presented at the IEEE International Conference on Systems, Man and Cybernetics, The Hague, Netherlands, 10-13 October, 2004.

ANFIS Based Modeling and Control of Nonlinear Systems: A Tutorial

Blindsight is the residual visuo-motor ability without subjective awareness observed after lesions of the primary visual cortex (V1). Various visual functions are retained, however, instrumental visual associative learning remains to be investigated. Here we examined the secondary reinforcing properties of visual cues presented to the hemianopic field of macaque monkeys with unilateral V1 lesions. Our aim was to test the potential role of visual pathways bypassing V1 in reinforcing visual instrumental learning. When learning the location of a hidden area in an oculomotor search task, conditioned visual cues presented to the lesion-affected hemifield operated as an effective secondary reinforcer. We noted that not only the hidden area location, but also the vector of the saccade entering the target area was reinforced. Importantly, when the visual reinforcement signal was presented in the lesion-affected field, the monkeys continued searching, as opposed to stopping when the cue was presented in the intact field. This suggests the monkeys were less confident that the target location had been discovered when the reinforcement cue was presented in the affected field. These results indicate that the visual signals mediated by the residual visual pathways after V1 lesions can access fundamental reinforcement mechanisms but with impaired visual awareness.

/pdf/visual-instrumental-learning-in-blindsight-monkeys-3xpauyxksg.pdf

Abdelhafid Zeghbib

Papers

Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals

ANFIS based modelling and control of non-linear systems : a tutorial

Modeling and control of non-linear systems using soft computing techniques

ANFIS Based Modeling and Control of Nonlinear Systems: A Tutorial

Visual instrumental learning in blindsight monkeys.