Consistency of Muscle Synergies Extracted via Higher-Order Tensor Decomposition Towards Myoelectric Control

In recent years, muscle synergies have been pro-posed for proportional myoelectric control. Synergies were extracted using matrix factorisation techniques (mainly non-negative matrix factorisation, NMF), which requires identification of synergies to tasks or movements. In addition, NMF methods were viable only with a task dimension of 2 degrees of freedoms(DoFs). Here, the potential use of a higher-order tensor model for myoelectric control is explored. We assess the ability of a constrained Tucker tensor decomposition to estimate consistent synergies when the task dimensionality is increased up to 3-DoFs. Synergies extracted from 3rd-order tensor of 1 and 3 DoFs were compared. Results showed that muscle synergies extracted via constrained Tucker decomposition were consistent with the increase of task-dimension. Hence, these results support the consideration of proportional 3-DoF myoelectric control based on tensor decompositions

Higher order tensor decomposition for proportional myoelectric control based on muscle synergies

In the recent years, muscle synergies have been utilised to provide simultaneous and proportional myoelectric control systems. All of the proposed synergy-based systems relies on matrix factorisation methods to extract the muscle synergies which is limited in terms of task-dimensionality. Here, we seek to demonstrate and discuss the potential of higher-order tensor decompositions as a framework to estimate muscle synergies for proportional myoelectric control. We proposed synergy-based myoelectric control model by utilising muscle synergies extracted by a novel \ac{ctd} technique. Our approach is compared with \ac{NMF} \ac{SNMF}, the current state-of-the-art matrix factorisation models for synergy-based myoelectric control systems. Synergies extracted from three techniques where used to estimate control signals for wrist's \ac{dof} through regression. The reconstructed control signals where evaluated by real glove data that capture the wrist's kinematics. The proposed \ac{ctd} model results was slightly better than matrix factorisation methods. The three models where compared against random generated synergies and all of them were able to reject the null hypothesis. This study provides demonstrate the use of higher-order tensor decomposition in proportional myoelectric control and highlight the potential applications and advantages of using higher-order tensor decomposition in muscle synergy extraction

Higher-order tensor decompositions for muscle synergy analysis

This doctoral thesis outlines several methodological advances in the application of higher-order tensor decomposition for muscle synergy analysis estimated from surface Electromyogram (EMG). This entails both assessing current muscle synergy extraction methods and a novel direct approach to estimate useful muscle synergies using higher-order tensor decomposition. The underlying hypothesis is that higher-order tensor decompositions provide advantages in the estimation of temporal profiles and muscle synergies thanks to the consideration of other domains such as spectral, task or repetition information. Moreover, we implement these advances to inspect potential applications of tensor synergies in biomechanical analysis and myoelectric control. Firstly, we provide an overview of the current mathematical models for the concept of muscle synergies and compare the common matrix factorisation methods for muscle synergy extraction, in addition to second-order blind identification (SOBI), a technique which has not been used for muscle synergy estimation previously. Synthetic and real EMG datasets related to wrist movements from the publicly available Ninapro dataset were used in this evaluation. Results suggest that a sparse synergy model and a higher number of channels would result in better-estimated synergies. SOBI has better performance when a limited number of electrodes is available, but its performance is still poor in that case. Overall, non-negative matrix factorisation (NMF) is the most appropriate method for synergy extraction and, therefore, it is considered as a benchmark in the rest of the thesis. We then show the benefits of higher-order tensor decompositions of EMG data for muscle synergy analysis, discussing possible 3rd and 4th-order tensors models for EMG data. We explore muscle synergy estimation from 4th-order EMG tensors by taking the spectral profile into account and utilise this model for classification between the wrist’s movements in comparison with NMF. The results provide a proof-of-concept for higher-order tensor decomposition as classification accuracy is slightly improved using tensor decomposition over NMF. However, the addition of spectral mode -with time-frequency analysis- increases the computational cost for tensor synergy estimation. After the previous proof of concept, we focus on the 3rd -order tensor model for efficient and reliable extraction of meaningful muscle synergies. The most prominent tensor decomposition models (Tucker and PARAFAC) are compared under different constraints. We notice that unconstrained Tucker decomposition cannot extract unique and consistent muscle synergies as it converges into different local minima, while PARAFAC model cannot deal with a higher number of synergies or tasks as the decomposition deviates from the trilinear model. As a result, we introduce a constrained Tucker decomposition model as a framework for muscle synergy analysis. The advantages of this method over NMF are highlighted in the biomechanical application of identifying shared and task-specific muscle synergies. This benefits from the natural multi-way form of the EMG data, which makes higher-order tensor decompositions a better option than applying matrix factorisation repetitively. The constrained Tucker decomposition can successfully identify shared and task-specific synergies and is robust to disarrangement regarding task-repetition information, unlike NMF. The constrained Tucker model is then used as a framework to extract synergistic information that could be applied to proportional upper limb myoelectric control. The consistency of extracted muscle synergies with the increase of the wrist’s task dimensionality into 3 degrees of freedom (DoF) is investigated in comparison with NMF. In the literature, NMF approaches for synergy-based proportional myoelectric control were viable only with a task dimension of 2 DoF. In contrast, the results show that a constrained Tucker model identifies consistent muscle synergies from 3-DoFs dataset directly. Moreover, a tensor-based approach for proportional myoelectric control is introduced and compared against NMF and sparse NMF as state of the art benchmarks. To sum up, higher-order tensor decomposition had not been utilised in EMG analysis despite the substantial attention it received in biomedical signal processing applications in recent years. This thesis explores higher-order tensor decompositions for synergy extraction to account for the natural multi-way structure of EMG data. We hope that it will pave the way for the development of muscle activity analysis methods based on higher-order techniques in broader applications

Synergy-Based Human Grasp Representations and Semi-Autonomous Control of Prosthetic Hands

Das sichere und stabile Greifen mit humanoiden Roboterhänden stellt eine große Herausforderung dar. Diese Dissertation befasst sich daher mit der Ableitung von Greifstrategien für Roboterhände aus der Beobachtung menschlichen Greifens. Dabei liegt der Fokus auf der Betrachtung des gesamten Greifvorgangs. Dieser umfasst zum einen die Hand- und Fingertrajektorien während des Greifprozesses und zum anderen die Kontaktpunkte sowie den Kraftverlauf zwischen Hand und Objekt vom ersten Kontakt bis zum statisch stabilen Griff. Es werden nichtlineare posturale Synergien und Kraftsynergien menschlicher Griffe vorgestellt, die die Generierung menschenähnlicher Griffposen und Griffkräfte erlauben. Weiterhin werden Synergieprimitive als adaptierbare Repräsentation menschlicher Greifbewegungen entwickelt. Die beschriebenen, vom Menschen gelernten Greifstrategien werden für die Steuerung robotischer Prothesenhände angewendet. Im Rahmen einer semi-autonomen Steuerung werden menschenähnliche Greifbewegungen situationsgerecht vorgeschlagen und vom Nutzenden der Prothese überwacht

The influence of musculoskeletal pain disorders on muscle synergies—A systematic review

Background Musculoskeletal (MSK) pain disorders represent a group of highly prevalent and often disabling conditions. Investigating the structure of motor variability in response to pain may reveal novel motor impairment mechanisms that may lead to enhanced management of motor dysfunction associated with MSK pain disorders. This review aims to systematically synthesize the evidence on the influence of MSK pain disorders on muscle synergies. Methods Nine electronic databases were searched using Medical Subject Headings and keywords describing pain, electromyography and synergies. Relevant characteristics of included studies were extracted and assessed for generalizability and risk of bias. Due to the significant heterogeneity, a qualitative synthesis of the results was performed. Results The search resulted in a total of 1312 hits, of which seven articles were deemed eligible. There was unclear consistency that pain reduced the number of muscle synergies. There were low consistencies of evidence that the synergy vector (W weights) and activation coefficient (C weights) differed in painful compared to asymptomatic conditions. There was a high consistency that muscle synergies were dissimilar between painful and asymptomatic conditions. Conclusions MSK pain alters the structure of variability in muscle control, although its specific nature remains unclear. Greater consistency in muscle synergy analysis may be achieved with appropriate selection of muscles assessed and ensuring consistent achievement of motor task outcomes. Synergy analysis is a promising method to reveal novel understandings of altered motor control, which may facilitate the assessment and treatment of MSK pain disorders

Electronic systems for the restoration of the sense of touch in upper limb prosthetics

In the last few years, research on active prosthetics for upper limbs focused on improving the human functionalities and the control. New methods have been proposed for measuring the user muscle activity and translating it into the prosthesis control commands. Developing the feed-forward interface so that the prosthesis better follows the intention of the user is an important step towards improving the quality of life of people with limb amputation. However, prosthesis users can neither feel if something or someone is touching them over the prosthesis and nor perceive the temperature or roughness of objects. Prosthesis users are helped by looking at an object, but they cannot detect anything otherwise. Their sight gives them most information. Therefore, to foster the prosthesis embodiment and utility, it is necessary to have a prosthetic system that not only responds to the control signals provided by the user, but also transmits back to the user the information about the current state of the prosthesis. This thesis presents an electronic skin system to close the loop in prostheses towards the restoration of the sense of touch in prosthesis users. The proposed electronic skin system inlcudes an advanced distributed sensing (electronic skin), a system for (i) signal conditioning, (ii) data acquisition, and (iii) data processing, and a stimulation system. The idea is to integrate all these components into a myoelectric prosthesis. Embedding the electronic system and the sensing materials is a critical issue on the way of development of new prostheses. In particular, processing the data, originated from the electronic skin, into low- or high-level information is the key issue to be addressed by the embedded electronic system. Recently, it has been proved that the Machine Learning is a promising approach in processing tactile sensors information. Many studies have been shown the Machine Learning eectiveness in the classication of input touch modalities.More specically, this thesis is focused on the stimulation system, allowing the communication of a mechanical interaction from the electronic skin to prosthesis users, and the dedicated implementation of algorithms for processing tactile data originating from the electronic skin. On system level, the thesis provides design of the experimental setup, experimental protocol, and of algorithms to process tactile data. On architectural level, the thesis proposes a design ow for the implementation of digital circuits for both FPGA and integrated circuits, and techniques for the power management of embedded systems for Machine Learning algorithms

Nonlinear and factorization methods for the non-invasive investigation of the central nervous system

This thesis focuses on the functional study of the Central Nervous System (CNS) with non-invasive techniques. Two different aspects are investigated: nonlinear aspects of the cerebrovascular system, and the muscle synergies model for motor control strategies. The main objective is to propose novel protocols, post-processing procedures or indices to enhance the analysis of cerebrovascular system and human motion analysis with noninvasive devices or wearable sensors in clinics and rehabilitation. We investigated cerebrovascular system with Near-infrared Spectroscopy (NIRS), a technique measuring blood oxygenation at the level of microcirculation, whose modification reflects cerebrovascular response to neuronal activation. NIRS signal was analyzed with nonlinear methods, because some physiological systems, such as neurovascular coupling, are characterized by nonlinearity. We adopted Empirical Mode Decomposition (EMD) to decompose signal into a finite number of simple functions, called Intrinsic Mode Functions (IMF). For each IMF, we computed entropy-based features to characterize signal complexity and variability. Nonlinear features of the cerebrovascular response were employed to characterize two treatments. Firstly, we administered a psychotherapy called eye movement desensitization and reprocessing (EMDR) to two groups of patients. The first group performed therapy with eye movements, the second without. NIRS analysis with EMD and entropy-based features revealed a different cerebrovascular pattern between the two groups, that may indicate the efficacy of the psychotherapy when administered with eye movements. Secondly, we administered ozone autohemotherapy to two groups of subjects: a control group of healthy subjects and a group of patients suffering by multiple sclerosis (MS). We monitored the microcirculation with NIRS from oxygen-ozone injection up 1.5 hours after therapy, and 24 hours after therapy. We observed that, after 1.5 hours after the ozonetherapy, oxygenation levels improved in both groups, that may indicate that ozonetherapy reduced oxidative stress level in MS patients. Furthermore, we observed that, after ozonetherapy, autoregulation improved in both groups, and that the beneficial effects of ozonetherapy persisted up to 24 hours after the treatment in MS patients. Due to the complexity of musculoskeletal system, CNS adopts strategies to efficiently control the execution of motor tasks. A model of motor control are muscle synergies, defined as functional groups of muscles recruited by a unique central command. Human locomotion was the object of investigation, due to its importance for daily life and the cyclicity of the movement. Firstly, by exploiting features provided from statistical gait analysis, we investigated consistency of muscle synergies. We demonstrated that synergies are highly repeatable within-subjects, reinforcing the hypothesis of modular control in motor performance. Secondly, in locomotion, we distinguish principal from secondary activations of electromyography. Principal activations are necessary for the generation of the movement. Secondary activations generate supplement movements, for instance slight balance correction. We investigated the difference in the motor control strategies underlying muscle synergies of principal (PS) and secondary (SS) activations. We found that PS are constituted by a few modules with many muscles each, whereas SS are described by more modules than PS with one or two muscles each. Furthermore, amplitude of activation signals of PS is higher than SS. Finally, muscle synergies were adopted to investigate the efficacy of rehabilitation of stiffed-leg walking in lower back pain (LBP). We recruited a group of patients suffering from non-specific LBP stiffening the leg at initial contact. Muscle synergies during gait were extracted before and after rehabilitation. Our results showed that muscles recruitment and consistency of synergies improved after the treatment, showing that the rehabilitation may affect motor control strategies

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