Discrete Wavelet Transform Analysis of Surface Electromyography for the Objective Assessment of Neck and Shoulder Muscle Fatigue

Objective assessment of neuromuscular fatigue caused by sub-maximal repetitive exertions is essential for the early detection and prevention of risks of neck and shoulder musculoskeletal disorders. In recent years, discrete wavelet transforms (DWT) of surface electromyography (SEMG) has been used to evaluate muscle fatigue, especially during dynamic contractions when the SEMG signal is non-stationary. However, its application to neck muscle fatigue assessment is not well established. Therefore, the purpose of this study was to establish DWT analysis as a suitable method to conduct quantitative assessment of neck muscle fatigue caused by dynamic exertions. Ten human participants performed 40 minutes of fatiguing repetitive arm and neck exertions. SEMG data from the upper trapezius and sternocleidomastoid muscles were recorded. Ten most commonly used orthogonal wavelet functions were used to conduct DWT analysis. A significant increase in the power was observed at lower frequency bands of 6-12Hz, 12-23 Hz, and 23-46 Hz with the onset and development of fatigue for most of the wavelet functions. Among ten wavelet function, a relatively higher power estimation, consistent statistical trend and better power contrast with the onset and development of fatigue was observed for the Rbio3.1 wavelet function. The results of this study will assist Professional Ergonomists to automate the process of localized muscle fatigue estimation, which could have applications related to improving working environment

Surface EMG and muscle fatigue: multi-channel approaches to the study of myoelectric manifestations of muscle fatigue

In a broad view, fatigue is used to indicate a degree of weariness. On a muscular level, fatigue posits the reduced capacity of muscle fibres to produce force, even in the presence of motor neuron excitation via either spinal mechanisms or electric pulses applied externally. Prior to decreased force, when sustaining physically demanding tasks, alterations in the muscle electrical properties take place. These alterations, termed myoelectric manifestation of fatigue, can be assessed non-invasively with a pair of surface electrodes positioned appropriately on the target muscle; traditional approach. A relatively more recent approach consists of the use of multiple electrodes. This multi-channel approach provides access to a set of physiologically relevant variables on the global muscle level or on the level of single motor units, opening new fronts for the study of muscle fatigue; it allows for: (i) a more precise quantification of the propagation velocity, a physiological variable of marked interest to the study of fatigue; (ii) the assessment of regional, myoelectric manifestations of fatigue; (iii) the analysis of single motor units, with the possibility to obtain information about motor unit control and fibre membrane changes. This review provides a methodological account on the multi-channel approach for the study of myoelectric manifestation of fatigue and on the experimental conditions to which it applies, as well as examples of their current applications

Current state of digital signal processing in myoelectric interfaces and related applications

This review discusses the critical issues and recommended practices from the perspective of myoelectric interfaces. The major benefits and challenges of myoelectric interfaces are evaluated. The article aims to fill gaps left by previous reviews and identify avenues for future research. Recommendations are given, for example, for electrode placement, sampling rate, segmentation, and classifiers. Four groups of applications where myoelectric interfaces have been adopted are identified: assistive technology, rehabilitation technology, input devices, and silent speech interfaces. The state-of-the-art applications in each of these groups are presented.Peer reviewe

Kohti yläraaja-proteesien ohjausta pintaelektromyografialla

The loss of an upper limb is a life-altering accident which makes everyday life more difficult.A multifunctional prosthetic hand with an user-friendly control interface may significantlyimprove the life quality of amputees. However, many amputees do not use their prosthetichand regularly because of its low functionality, and low controllability. This situation callsfor the development of versatile prosthetic limbs that allow amputees to perform tasks thatare necessary for activities of daily living. The non-pattern based control scheme of the commercial state-of art prosthesis is rather poorand non-natural. Usually, a pair of muscles is used to control one degree of freedom. Apromising alternative to the conventional control methods is the pattern-recognition-basedcontrol that identifies different intended hand postures of the prosthesis by utilizing theinformation of the surface electromyography (sEMG) signals. Therefore, the control of theprosthesis becomes natural and easy. The objective of this thesis was to find the features that yield the highest classificationaccuracy in identifying 7 classes of hand postures in the context of Linear DiscriminantClassifier. The sEMG signals were measured on the skin surface of the forearm of the 8 ablebodiedsubjects. The following features were investigated: 16 time-domain features, twotime-serial-domain features, the Fast Fourier Transform (FFT), and the Discrete WaveletTransform (DWT). The second objective of this thesis was to study the effect of the samplingrate to the classification accuracy. A preprocessing technique, Independent ComponentAnalysis (ICA), was also shortly examined. The classification was based on the steady statesignal. The signal processing, features, and classification were implemented with Matlab. The results of this study suggest that DWT and FFT did not outperform the simple andcomputationally efficient time domain features in the classification accuracy. Thus, at least innoise free environment, the high classification accuracy (> 90 %) can be achieved with asmall number of simple TD features. A more reliable control may be achieved if the featuresare selected individually of a subset of the effective features. Using the sampling rate of 400Hz instead of commonly used 1 kHz may not only save the data processing time and thememory of the prosthesis controller but also slightly improve the classification accuracy.ICA was not found to improve the classification accuracy, which may be because themeasurement channels were placed relatively far from each other.Yläraaja-amputaatio vaikuttaa suuresti päivittäiseen elämään. Helposti ohjattavalla toiminnallisillaproteeseilla amputoitujen henkilöiden elämänlaatua voitaisiin parantaa merkittävästi.Suurin osa amputoiduista henkilöistä ei kuitenkaan käytä proteesiaan säännöllisesti proteesinvähäisten toimintojen ja vaikean ohjattavuuden vuoksi. Olisikin tärkeää kehittää helpostiohjattava ja riittävästi toimintoja sisältävä proteesi, joka mahdollistaisi päivittäisessäelämässä välttämättömien tehtävien suorittamisen. Markkinoilla olevat lihassähköiset yläraajaproteesit perustuvat yksinkertaiseen hahmontunnistustahyödyntämättömään ohjaukseen, jossa lihasparilla ohjataan yleensä yhtä proteesinvapausastetta. Lupaava vaihtoehto perinteisille ohjausmenetelmille on hahmontunnistukseenpohjautuva ohjaus. Se tunnistaa käyttäjän käden asennot käsivarren iholta mitatun lihassähkösignaalinsisältämän informaation avulla mahdollistaen helpon ja luonnollisen ohjauksen. Tämän diplomityön tavoitteena oli löytää piirteet, jolla seitsemän erilaista käden asentoa pystytäänluokittelemaan mahdollisimman tarkasti lineaarisella diskriminantti luokittelijalla.Lihassähkösignaalit mitattiin kahdeksan ei-amputoidun koehenkilön käsivarresta ihon pinnallekiinnitetyillä elektrodeilla. Työssä vertailtiin seuraavia piirteitä: 16 aika-alueen piirrettä,kaksi aikasarja-alueen piirrettä, nopea Fourier-muunnos (FFT), diskreetti Aallokemuunnos(DWT). Työn toinen tavoite oli tutkia näytteenottotaajuuden vaikutusta luokittelutarkkuuteen.Myös esiprosessointia riippumattomien komponenttien analyysillä tutkittiinlyhyesti. Luokittelu tehtiin staattisen lihassupistuksen aikana mitatun signaalin perusteella.Signaalin prosessointi, piirteet ja luokittelu toteutettiin Matlabilla. Tämän tutkimuksen tulokset osoittivat, etteivät diskreetti Aalloke-muunnos ja nopea Fouriermuunnosyllä laskennallisesti tehokkaampia aika-alueen piirteitä parempaan luokittelutarkkuuteen.Pienellä määrällä yksinkertaisia aika-alueen piirteitä voidaan saavuttaa hyvä luokittelutarkkuus(>90 %). Luokittelutarkkuutta voitaneen edelleen parantaa valitsemalla optimaalisetpiirteet yksilöllisesti pienestä joukosta hyviksi havaittuja piirteitä. Käyttämällä 400Hz:n näytteenottotaajuutta yleisesti käytetyn 1 kHz:n sijasta, voidaan sekä säästää prosessointiaikaaja proteesin prosessorin muistia että myös parantaa hieman luokittelutarkkuutta.Esiprosessointi riippumattomien komponenttien analyysillä ei parantanut luokittelutarkkuutta,mikä johtunee siitä, että mittauskanavat olivat suhteellisen kaukana toisistaan

AN INVESTIGATION OF ELECTROMYOGRAPHIC (EMG) CONTROL OF DEXTROUS HAND PROSTHESES FOR TRANSRADIAL AMPUTEES

In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Plymouth University's products or services.There are many amputees around the world who have lost a limb through conflict, disease or an accident. Upper-limb prostheses controlled using surface Electromyography (sEMG) offer a solution to help the amputees; however, their functionality is limited by the small number of movements they can perform and their slow reaction times. Pattern recognition (PR)-based EMG control has been proposed to improve the functional performance of prostheses. It is a very promising approach, offering intuitive control, fast reaction times and the ability to control a large number of degrees of freedom (DOF). However, prostheses controlled with PR systems are not available for everyday use by amputees, because there are many major challenges and practical problems that need to be addressed before clinical implementation is possible. These include lack of individual finger control, an impractically large number of EMG electrodes, and the lack of deployment protocols for EMG electrodes site selection and movement optimisation. Moreover, the inability of PR systems to handle multiple forces is a further practical problem that needs to be addressed. The main aim of this project is to investigate the research challenges mentioned above via non-invasive EMG signal acquisition, and to propose practical solutions to help amputees. In a series of experiments, the PR systems presented here were tested with EMG signals acquired from seven transradial amputees, which is unique to this project. Previous studies have been conducted using non-amputees. In this work, the challenges described are addressed and a new protocol is proposed that delivers a fast clinical deployment of multi-functional upper limb prostheses controlled by PR systems. Controlling finger movement is a step towards the restoration of lost human capabilities, and is psychologically important, as well as physically. A central thread running through this work is the assertion that no two amputees are the same, each suffering different injuries and retaining differing nerve and muscle structures. This work is very much about individualised healthcare, and aims to provide the best possible solution for each affected individual on a case-by-case basis. Therefore, the approach has been to optimise the solution (in terms of function and reliability) for each individual, as opposed to developing a generic solution, where performance is optimised against a test population. This work is unique, in that it contributes to improving the quality of life for each individual amputee by optimising function and reliability. The main four contributions of the thesis are as follows: 1- Individual finger control was achieved with high accuracy for a large number of finger movements, using six optimally placed sEMG channels. This was validated on EMG signals for ten non-amputee and six amputee subjects. Thumb movements were classified successfully with high accuracy for the first time. The outcome of this investigation will help to add more movements to the prosthesis, and reduce hardware and computational complexity. 2- A new subject-specific protocol for sEMG site selection and reliable movement subset optimisation, based on the amputee’s needs, has been proposed and validated on seven amputees. This protocol will help clinicians to perform an efficient and fast deployment of prostheses, by finding the optimal number and locations of EMG channels. It will also find a reliable subset of movements that can be achieved with high performance. 3- The relationship between the force of contraction and the statistics of EMG signals has been investigated, utilising an experimental design where visual feedback from a Myoelectric Control Interface (MCI) helped the participants to produce the correct level of force. Kurtosis values were found to decrease monotonically when the contraction level increased, thus indicating that kurtosis can be used to distinguish different forces of contractions. 4- The real practical problem of the degradation of classification performance as a result of the variation of force levels during daily use of the prosthesis has been investigated, and solved by proposing a training approach and the use of a robust feature extraction method, based on the spectrum. The recommendations of this investigation improve the practical robustness of prostheses controlled with PR systems and progress a step further towards clinical implementation and improving the quality of life of amputees. The project showed that PR systems achieved a reliable performance for a large number of amputees, taking into account real life issues such as individual finger control for high dexterity, the effect of force level variation, and optimisation of the movements and EMG channels for each individual amputee. The findings of this thesis showed that the PR systems need to be appropriately tuned before usage, such as training with multiple forces to help to reduce the effect of force variation, aiming to improve practical robustness, and also finding the optimal EMG channel for each amputee, to improve the PR system’s performance. The outcome of this research enables the implementation of PR systems in real prostheses that can be used by amputees.Ministry of Higher Education and Scientific Research and Baghdad University- Baghdad/Ira

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

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level