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Projects / Programmes source: ARIS

Exact quantification of muscle control strategies and co-activation patterns in robot-assisted rehabilitation of hemiparetic patients

Research activity

Code Science Field Subfield
2.06.07  Engineering sciences and technologies  Systems and cybernetics  Biomedical technics 

Code Science Field
B115  Biomedical sciences  Biomechanics, cybernetics 

Code Science Field
2.06  Engineering and Technology  Medical engineering  
Keywords
muscle control strategies, muscle synergies, robot-assisted rehabilitation, stroke, surface electromyogram, dynamic muscle contractions, motor units
Evaluation (rules)
source: COBISS
Researchers (14)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  20180  PhD Imre Cikajlo  Systems and cybernetics  Researcher  2016 - 2018  259 
2.  21537  PhD Matjaž Divjak  Computer science and informatics  Researcher  2016 - 2018  104 
3.  31095  PhD Vojko Glaser  Systems and cybernetics  Researcher  2016 - 2017  50 
4.  21301  PhD Aleš Holobar  Systems and cybernetics  Head  2016 - 2018  501 
5.  12156  PhD Danilo Korže  Systems and cybernetics  Researcher  2016 - 2018  209 
6.  39011  Jernej Kranjec  Systems and cybernetics  Researcher  2018  38 
7.  14038  PhD Zlatko Matjačić  Systems and cybernetics  Researcher  2016 - 2018  370 
8.  36506  PhD Uroš Mlakar  Computer science and informatics  Researcher  2018  64 
9.  21601  Jurij Munda    Technical associate  2016 - 2018  33 
10.  24473  PhD Andrej Olenšek  Systems and cybernetics  Researcher  2016 - 2018  113 
11.  15801  PhD Božidar Potočnik  Systems and cybernetics  Researcher  2016 - 2018  312 
12.  36164  Martin Šavc  Systems and cybernetics  Researcher  2016 - 2018  63 
13.  32077  PhD Matjaž Zadravec  Systems and cybernetics  Researcher  2016 - 2018  72 
14.  08061  PhD Damjan Zazula  Systems and cybernetics  Researcher  2016 - 2018  789 
Organisations (2)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0309  University Rehabilitation Institute, Republic of Slovenia  Ljubljana  5053919000  5,739 
2.  0796  University of Maribor, Faculty of Electrical Engineering and Computer Science  Maribor  5089638003  27,534 
Abstract
Human movements are still poorly understood, mainly due to their high complexity and large number of degrees of freedom embedded in neural commands. This shortcoming can be compensated by analyzing electrical activity of muscles at the surface of the skin, so called surface electromyogram (EMG). The latter offers numerous advantages over clinically more established intramuscular investigations, such as repeatability of measurements, patient’s comfort and acceptance of investigation technique, no risk of infection and lower investigation and examination costs. For these reasons surface EMG has been extensively used in the fields of neuroscience, rehabilitation, pathophysiological investigations, training of the athletes and in man-machine interfacing, especially for control of active prosthesis. The main challenge in all these applications is accurate identification of neural commands out of recorded EMG. Namely, EMG is composed of many action potentials that are contributed by basic functional units of a muscle, so called motor units (MUs). Central nervous system controls muscle force by controlling the number of active MUs and their discharge rates. Although playing a vital role in EMG, the shape of action potentials is completely irrelevant from the central control viewpoint. Moreover, due to the polyphasic MU action potential shape and asynchronous MU activity, the recorded surface EMG appears to be highly interferential and the muscle activations are typically estimated by calculating the EMG energy envelops. This offers limited insight into the neural commands of skeletal muscles, mainly due to sensitivity of EMG signals on the shape of MU action potentials. The latter depends on muscle anatomy, subcutaneous tissue and contraction level and is one of the main reasons for large variability and low repeatability of surface EMG signals reported in the literature. This negative impact of motor unit action potentials has been largely ignored in the studies of motor behavior, mainly due to the lack of suitable signal processing techniques. Even more, EMG envelopes have been used to establish a widespread theory of muscle synergies. The latter hypothesizes that a shared co-activation of two or more muscles is modulated by a single neural command from CNS. This is believed to simplify the control of human movements. In this project, we propose to solve the aforementioned problem of EMG processing by separating the information on the shape of MU action potentials from the information on MU discharge patterns. We then use this novel methodology to study the muscle excitation primitives in upper limb movements of healthy and hemiparetic subjects. In particular, we challenge the current muscle synergies estimation from both methodological and physiological viewpoints. From methodological viewpoint, it is not clear to what extent the identified muscle activation patterns reflect the common geometrical changes in investigated muscles rather their excitation commands. From the physiologic viewpoint, it has been demonstrated that the residual 10% - 15% portion of the signal variability, not accounted for by current muscle synergy estimations, carries the information that is crucial for reduction of functional movement errors. This phenomenon is currently unexplained. The proposed project aims at clarifying all aforementioned issues. In order to demonstrate their robustness and suitability for analysis of central nervous system disorders, the developed techniques will be used to accurately track the pathological variability in excitation of individual upper limb muscles in the hamiparetic patients and to compare this variability to the functional movement errors as assessed by a haptically-controlled UHD rehabilitation robot. This is expected to provide better information support to rehabilitation decisions and, thus, maximize the patient’s rehabilitation potential.
Significance for science
There is currently 886 publications (estimated from PubMed.gov) in peer reviewed journals on muscle synergies and the number of publications per year is steeply increasing. In practically all these studies, the classical estimation of muscle excitation primitives by decomposition of EMG energy envelopes has been employed. Therefore, all these studies suffer form methodological limitations presented in this proposal. Solving these methodological problems would have a huge impact on this scientific field, contributing considerably to the visibility and promotion of Slovenian research excellence. Also the use of intelligent devices/robots has drastically increased in rehabilitation medicine. Robot assisted therapy is assumed to help patients relearn motor control, but pure robot-assisted mechanical rehabilitation often results in non-functional movement patterns as it is mainly limited to retraining of the peripheral nervous system (PNS). Moreover, it is still poorly understood why different rehabilitation interventions work for certain patients and not for others. Stroke affects brain structures, i.e. the central nervous system (CNS). This suggests that both PNS and CNS need to be integrated in a sound physical rehabilitation therapy. The proposed project effectively addresses these challenges. CNS functionality can be assessed also from brain-computer interfaces, but the latter are limited by the extent and reliability of extracted information. When it comes to motor rehabilitation, skeletal muscles can be seen as natural amplifiers of motor codes send by CNS. This approach, proposed in this project, allows for more accurate, faster and more robust motor code estimation than existing brain-computer interfaces, but has not yet been employed in motor rehabilitation as no technique for effective compensation of negative impact of MUAP shapes in dynamic EMG signals has been proposed so far. The proposed project addresses the problem of accurate muscle excitation estimation in a methodologically exact and highly efficient manner, that is, by separating the true muscle excitation patterns from the impacts of ever-changing muscle geometry and random motor unit distribution on surface EMG (Figures 1 and 2). The extracted information on muscle excitation patterns and the online feedback on the quality of their assessment present sound foundation for the implementation of advanced cognitive systems that will dynamically adapt to the user’s motor capabilities, fatigue and rehabilitation goals. In the proposed project, the effectiveness of newly proposed methodology will be demonstrated by analysing impairments in paretic muscles. The very same concept can easily be extended to numerous other pathologies and high-tech muscle-machine interface applications, boosting significantly the accuracy and performance of the state-of-the-art muscle-computer interfaces.
Significance for the country
Stroke is the third most common cause of death in Western society. In the European Union (EU), Iceland, Norway, and Switzerland an estimated 1.1 million new stroke events occur each year and currently 6 million subjects live in these countries having survived a stroke. For every decade after age of 55, the relative incidence of stroke doubles. Rates increase from approximately 5,000 per 100,000 in subjects aged less than 75 years to 10,000 per 100,000 in those aged 80+. According to population projections from the United Nations the number of new stroke events will increase to 1.5 million per year in 2025 in these countries. A total annual cost of stroke in Europe of 21,895,000,000 € is estimated. All this leads to a large and growing market for user-centric solutions in rehabilitation after stroke. It is expected that the impact of the proposed project will contribute to controlling and reduction of these costs. In particular, the results of the project will lay strong foundation for development of financially sustainable solutions and therapeutic protocols that may considerably enhance the outcome of rehabilitation processes targeting specifically the upper limb rehabilitation after stroke. Non-functional hemiparetic arm with exaggerated spasticity, which develops in the absence of adequate therapeutic treatment, represents substantial burden to the families of stroke survivors and indirectly to the whole society. The novel aspects of the proposed research targeting at “normalisation” of muscle excitation patterns of the upper limb have significant potential to advance the efficacy of robot-assisted rehabilitation. Besides stroke patients, the developed tools are expected to have an impact on the persons with muscle weakness or paralysis, resulting from diseases of the central nervous system, or neurological damage resulting from spinal cord injuries. Muscle weakness or stiff muscles affects 40 % to 60 % of patients with spinal cord injuries (10.600 new cases each year in Europe) and average lifetime cost of spinal cord injuries is estimated between 380.000 € and 1.5 million €.  A better understanding of alternations in central muscle control strategies, as proposed in this project, will enable the design of more accurately targeted interventions. The knowledge gained will also lay down the foundation for next generation of muscle-machine interfaces that exert physical stimulation to reinforce cortical plasticity. Preliminary tests of such interfaces applied to targeted muscle reinnervation patients (TMR) have already been conducted by University of Maribor, University of Göttingen and Ottobock company[12]. The results demonstrated that complete identification of neural codes sent to the reinnervated muscle is possible by decomposing surface EMG signals during isometric muscle contractions. The proposed project will expand these methodological concepts to the dynamic conditions and, thus, significantly broaden their exploitation potential.
Most important scientific results Interim report, final report
Most important socioeconomically and culturally relevant results Interim report, final report
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