Apply for the mini-poster-session at this workshop, and get the chance to discuss your work with other people in the same field ( You also can attened the workshop without submitting abstract for the mini poster)
June 1, 2015 – Abstract Submission Starts
July 15, 2015 - Abstract Submission Ends
July 25, 2015 – Author Notification
July 29, 2015 – Author Final abstract Submission
Human behaviors are a result of complex neural dynamics between the central nervous system (CNS), proprioceptors, and the musculoskeletal system. The notion of muscle synergy, defined as relative weight of muscle activations driven by common excitation primitives, has received considerable attention from the neuroscience community as a way to interpret, in a quantitative way, the neural strategy adopted by the CNS to simplify the coordination of muscles. Muscle synergies have been well investigated in several areas including the: classifying and modeling human and animal motor skills, identifying the degree of brain damage after neurological lesion, and assisting stroke therapy. The current applications based on muscle synergy analysis have been mainly designed to work-offline, yet few efforts have been done to move forward and obtain online synergy. Online synergy computations can be utilized as a robust neurofeedback signaling that is essential to design effective stroke rehabilitation or skill-acquisition training programs by: assisting synergies reorganization, controlling exoskeleton robots, predicting models of human locomotion, controlling multi-degree of freedom prostheses, designing online robotic therapy, etc. This workshop, therefore, focuses on the possible methodologies to move beyond current off-line muscle synergy analysis discussing the possible solutions to overcome dilemmas and highlight real world applications of online muscle synergy. We believe that the outputs of this workshop will bring out the opportunity for new avenues in neurorehabilitation.
Fady S. Alnajjar & Shingo Shimoda
Intelligent Behavior Control Unit, Brain Science Institute (BSI) BSI-TOYOTA Collaboration Center (BTCC), RIKEN, Japan
Juan C Moreno & Diego Torricelli
Neural Rehabilitation Group, Cajal Institute, the Spanish National Research Council (CSIC), Madrid, Spain
INVITED SPEAKERS (Detailed Schedule)
1. Silvia Muceli, Dept. of Neurorehab. Eng., Uni. Med. Center Göttingen, Georg-August Univ., Germany.
Silvia received the MSc in Electronics Engineering at the University of Cagliari, Italy, in 2007, and the PhD at The International Doctoral School in Biomedical Science and Engineering, Center for Sensory-Motor Interaction (SMI), Aalborg University, Denmark, in 2013. Since 2011, she is working as a researcher at the Department of Neurorehabilitation Engineering, University Medical Center Göttingen, Georg-August University, Germany. Her main research interests concern surface and intramuscular electromyography, signal processing of biomedical signals and advanced prosthetic control.
Title: Robustness of a muscle synergy-based myoelectric control system
Myoelectric prostheses allow the control of one degree of freedom at the time. In commercial prostheses, the function selection is accomplished by a muscle co-contraction or a physical switch, which makes the control unintuitive. In research myoelectric control systems, the function selection is based on the pattern recognition approach which is not robust to electrode shifts, which may occur during donning/doffing of the prosthetic socket. In our laboratory, we exploited the muscle synergy model in designing strategies for robust and simultaneous control of multi-degree of freedom prostheses. This model assumes that the activation pattern of muscles in a task can be decomposed as profiles of relative activations across a group of muscles (synergies) and the neural commands that the muscle synergies receive (activation signals). In the myoelectric control scheme that we propose, activation signals extracted from multi-channel electromyogram recordings from the forearm muscles are directly used as control signals for online goal-directed control tasks involving simultaneous and proportional activation of the degrees of freedom of the wrist. The talk will focus on the online performance achievable with our method, with special emphasis of the robustness with respect to electrode number, electrode shift and electromyogram crosstalk.
3. Massimo Sartori, Univ. Med. Center Goettingen, Dept. of Neurorehab. Eng., Bernstein Center for Comp. Neurosci. Germany.
Massimo received his MSc in Computer Engineering and his PhD in Information and Communication Science and Technologies from the University of Padova, Italy in 2007 and 2011. During his PhD, he was a visiting student at the School of Sport Science, Exercise and Health, University of Western Australia and at the Neuromuscular Biomechanics Laboratory, Stanford University. After a research period in 2011, at the Centre for Musculoskeletal Research at the Griffith Health Institute, Griffith University in Australia, Dr. Sartori became a postdoctoral research scientist at the Department of Neurorehabilitation Engineering, University Medical Center Goettingen, Germany where he also is responsible of the Motor Physiology and Biomechanics Lab as well as of the Virtual Biomechanics Lab. In 2013, Dr Sartori was a Visiting Scholar at the National Center for Simulation in Rehabilitation Research (NCSRR) at Stanford University. In 2014 he received a NCSRR OpenSim Fellowship. Dr Sartori's research interests include the development of methods for bridging between the neural and the functional understanding of human movement in vivo, and the translation of these to the development of advanced neurorehabilitation technologies.
Title: Predictive models of muscle modularity and musculoskeletal dynamics
Recent research increasingly supports the hypothesis that muscle synergies, defined as relative weight of muscle excitations driven by functionally relevant multi-muscle co-excitation primitives, may reflect neural coordinative modules that reduce complexity of motor control. The modularity and the synergistic nature of muscle recruitment are traditionally investigated using a descriptive approach. This includes dimensionality reduction methods that approximate the structure and statistical distribution underlying experimental recordings of muscle excitations. Despite the progress made in this direction, it is unclear whether this approach can be used to predict (rather than describe) the neuromuscular control strategies underlying human movement. This contribution will outline methods for generating predictive models of human locomotion based on the theory of muscle synergies and modularity. Furthermore, it will discuss the perspectives for synthetizing the neuromuscular mechanisms underlying human locomotion and the implications in the field of simulation and neurorehabilitation technologies.
5. Diego Torricelli, Neural Rehabilitation Group, Cajal Institute, the Spanish National Research Council (CSIC), Madrid, Spain.
Diego received the MSc in Mechanical Engineering at the University of Roma in 2004. His Master’s thesis was developed in Harvard Medical School, Boston, in the Gait Analysis Lab of the Spaulding Rehab Hospital. In 2009 he received the PhD in Biomedical Engineering from University of Roma TRE, with a thesis focused on eye-driven interfaces for rehabilitation. In 2008 he co-founded a spin-off company for the development of innovative human-computer interfaces. He is now working at the Bioengineering Group of CSIC in Madrid, focusing on the study of human motor control principles and their application to technology-based neurorehabilitation.
Title: Online biofeedback of cycling performance based on muscle synergies
The use of biofeedback in cycling is gaining relevance as a way to improve the performance in both sport and clinical applications. Velocity and pedaling force are preferentially used to this purpose. More recently, muscle contraction showed good potential to be used as a third measure of user’s activity. This technique is usually applied on just a few muscles, therefore informing about a very limited portion of the muscular system. Muscle synergies can represent the coordination of many different muscles in a global and compact way. Therefore, their use as online feedback variables during cycling may provide more complete information to the user’s neuromuscular state. We will speculate about the use of this synergy-based biofeedback in clinical application, presenting preliminary experimental results in healthy people.
2. Qi An, Dept. of Precision Eng., School of Eng., Univ. of Tokyo, Tokyo, Japan.
Qi received his B.E., M.E., and PhD from the University of Tokyo, Japan, in 2009, 2011, and 2014. From 2010 to 2011, he joined Yoky Matsuoka’s Lab as a visiting student at University of Washington, USA. From Nov.2014 to Mar.2015, he studied Martin Buss’s Lab as a visiting researcher at Technische Universitat Munchen, Germany. He is currently a JSPS Research Fellowship for Young Scientists (PD) working on motor control theory of human standing-up motion. He is currently an assistant professor at the University of Tokyo. His research interest is to understand how humans modulate their muscle synergies to adapt different environments.
Title: Application of Muscle Synergy for Diagnosis and Rehabilitation System
In order to utilize the concept of muscle synergy for online rehabilitation, it is necessary to know impaired structure of muscle synergy from patient movements and to decide the direction of rehabilitation. However, muscle synergy structure varied among patients, and it is not always easy to measure their muscle activity to identify synergy structure. In order to solve this problem, our study has focused on human standing-up motion and constructed a database of resultant movement from impaired muscle synergy structure. This database is constructed from forward dynamic simulation of human musculoskeletal model to calculate how standing-up motion changes according to the different structure of muscle synergy. It can therefore elucidate that what muscle synergies resulted in failure motion (e.g. falling down or unable to lift up their body) and how each synergy contributes to success of the standing-up motion. The database enables health care provider to detect impaired motor function from observed movement and it also suggests the rehabilitation direction to improve body function.
3. Andrea d'Avella, Lab. of Neuromotor Physiology at Santa Lucia Foundation, Rome, Italy.
Andrea obtained a B.Sc. in Physics at Milan University, and a Ph.D. in Neuroscience at M.I.T. (2000) working on the modular organization of the motor system under the supervision of Emilio Bizzi. In 2003 he joined the Laboratory of Neuromotor Physiology at Fondazione Santa Lucia, Rome, Italy. Since 2015 he is Professor of Physiology in the Department of Biomedical Sciences and Morphological and Functional Images at the University of Messina, Italy. His research is focused on investigating sensorimotor control of reaching and interceptive movements, muscle synergies, and motor adaptation. He has developed a decomposition algorithm to identify time-varying muscle synergies from multi-muscle EMG recordings and an approach using myoelectric control in a virtual environment (“virtual surgeries”) to directly probe the synergistic organization of the motor system. He has coordinated and participated in international research projects funded by the Human Frontiers Science Program Organization and by the European Union. He is member of the Board of Directors of the Society for the Neural Control of Movement, of the Editorial Boards of the Journal of Motor Behavior, the Journal of Neurophysiology and Frontiers in Computational Neuroscience.
Title: Remapping of muscle forces during myoelectric control of a virtual mass to assess synergistic organization and to assist rehabilitation
The central nervous system may organize muscle synergies to simplify motor control and muscle synergy structure or recruitment may be disrupted after neurological lesion. However, most of the evidence for synergistic organization and disruption is indirect because it comes from the decomposition of muscle patterns into a small number of synergy vectors. Using myoelectric control of a virtual mass, we developed a novel experimental approach to directly assess the synergistic organization of the muscle patterns underlying the generation of isometric forces at the hand. We tested the prediction that in a truly modular controller it must be harder to adapt to perturbations that are incompatible with the modules. After identifying muscle synergies, we altered the mapping between recorded muscle activity and simulated force applied on the mass, as in a complex surgical rearrangement of the tendons. As predicted by modularity, adaptation was faster after a remapping compatible with the synergies, i.e. such that a full range of forces could still be achieved recombining the synergies, than after a remapping incompatible with the synergies, i.e. requiring new or modified muscle coordination patterns. Myoelectric control and remapping of dysfunctional muscle patterns may be also used to assist motor recovery in stroke patients.
6. Fady S. Alnajjar, Intelligent Behavior Control Unit, Brain Science Institute (BSI) BSI-TOYOTA Collaboration Center (BTCC), RIKEN, Japan.
Fady received his MSc in Artificial Intelligence and his PhD in System Design Engineering at the University of Fukui, Japan in 2007 and 2010. Since 2010, he is a research scientist at brain science institute (BSI), RIKEN. He conducted neuro-robotics study with Prof. Jun Tani to understand the underlying mechanisms for embodied cognition and mind. Since 2012, he started to have an interest in exploring the neural mechanisms of motor learning, adaptation, and recovery after brain injury from the sensory- and muscle-synergies perspectives. His research target is to propose practical neurorehabilitation applications for patients with brain injuries.
Title: Muscle synergies as a neurofeedaback to assist motor function recovery
The notion of muscle synergy has been proposed to explain the neural strategy adopted by the central nervous system (CNS) to simplify dealing with the redundancy of our musculoskeletal systems. Muscle synergy defines the synchronization level of several muscles as low-dimensional modules, providing simple controller but yet allowing for complex motor behavior. Recent studies have reported that muscle synergy organizations were altered from their original states after brain stroke causing abnormal recruitments of muscles. Muscle synergy classifications, therefore, were recommended to overcome traditional clinical tests by providing greater diagnostic accuracy of the level of motor function recovery. Expanding on this research direction, here we are intending to answer the question: can we enforce directly the re-organization of muscle synergy by providing a visual feedback that shows the current synergy-state to the patient and supervises its modification direction either by visual or mechanical supports? We are developing online synergy computation protocols to feedback the participants, in a user friendly interface, with their current synergy status immediately after a completion of point-to-point motion. Virtual environments created by a robotic manipulator with a bilateral controller are used to apply forces to support synergy reorganization. A number of moderate stroke patients are recruited in this study and the recovery processes are evaluated for over three months. Initial results are promising, highlighting the potential of faster motor recovery compared with patients who engaged only in a regular rehabilitation course. We believe that this research direction may potentially offer guidance in the development of new rehabilitation approaches to treat neural disorders and help the patients to regain the capabilities for performing activities of daily living.