7th Annual International IEEE EMBS Conference on Neural Engineering Montpellier, France, 22 - 24 April, 2015
A Neuromuscular Electrical Stimulation Strategy Based on Muscle Synergy for Stroke Rehabilitation C. Zhuang, J.C. Marquez*, IEEE Member, H.E. Qu, X. He, IEEE Student Member, and N. Lan, IEEE Senior Member
Abstract— Recent experiments have suggested that the central nervous system (CNS) makes use of muscle synergies as a neural strategy to simplify the control of a variety of movements by using a single pattern of neural command signal. This nature of muscle coordination could have great significance in the treatment and rehabilitation of upper limb impairments for hemiparetic patients post stroke. The use of neuromuscular electrical stimulation (NMES) for neural prosthetics or therapeutic applications has been demonstrated as a promising clinical intervention for stroke patients to recover motor function of the upper extremity. However, the existing NMES systems do not provide control methods for the patient to achieve an individualized and functional rehabilitation training. In this research work, muscle synergies from the flexionextension elbow antagonistic muscles were studied. Using motion information and EMG signals, muscle synergies were extracted using non-negative matrix factorization (NMF) method. Reconstructed signals obtained from the muscle synergies were then applied to the virtual arm (VA) model to test a synergy based NMES strategy. Results show close resemblance to the original elbow trajectory of normal movements and thus the feasibility to control movements in stroke patients for rehabilitation.
I. INTRODUCTION Human upper arm reaching movements involve complex non-linearity dynamics, such as interactions between joint motions and the activation of redundant muscles. Control theories and strategies have been suggested for central nervous system (CNS) based on a hierarchical and modular organization [1-3]. The common denominator of these theories is the simplification of motor control based on a
This research is supported in part by a grant from the Natural Science Foundation of China (No. 81271684). C. Zhuang, H.E. Qu, and X. He are with Institute of Rehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road, Shanghai, 200030 China (e-mail:
[email protected];
[email protected]). N. Lan is with Institute of Rehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road, Shanghai, 200030 China, and Division of Biokinesiology and physical Therapy, University of Southern California, Los Angeles, USA (email:
[email protected],
[email protected]). J.C.Marquez is with Institute of Rehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road, Shanghai, 200030 China, and School of Technology and Health at Royal Institute of Technology, SE-141 52 Huddinge, Sweden. (*communication author, e-mail:
[email protected]).
978-1-4673-6389-1/15/$31.00 ©2015 IEEE
reduction of redundant degrees of freedom (DOF) involved in the control. In recent years a theory that has gained strong acceptance is the concept of muscle synergies where a modular and coordinated activation of muscles is encoded by a control signal from the CNS that eventually will lead to a specific movement [4-6]. The extraction of muscle synergies can be reached through different algorithms, in this research work Non-negative Matrix Factorization (NMF) was used since it has been considered a suitable approach according to the literature [7]. The estimation of muscle synergies following NMF method has been defined through different conceptual approaches that basically differ in the mathematical model used to factorize the EMG recordings into a time and/or spatial frame. The most commonly used of these approaches are known as, temporal, synchronous and time varying muscle synergies [4-6]. This method was used in our studies to obtained patterns of muscle synergies. NMES for therapeutic applications is a technology regularly used in patients with stroke and other neurological disorders for improving muscle functionality [6, 7]. However, NMES can also be used for functional or neuroprosthetic applications in order to restore motor functions by activating paralyzed muscles in a controlled sequence and magnitude necessary to accomplish functional tasks [8, 9]. Yet the full potential has been hindered by the lack of an optimal way of activating a set of muscles and significant barriers still remain in clinical applications [10]. The purpose of this study is to test a new control strategy of NMES therapy for stroke rehabilitation based on the concept of muscle synergy of CNS control of movements. The synergy-based strategy was applied to control single joint elbow movements by using a virtual arm (VA) model [11]. Results indicated that the new strategy is capable of reproducing a range of elbow movements by scaling the synergy pattern. This suggested the feasibility of an adaptive control by simply adjusting scaling weights of synergy patterns. II. MATERIAL & METHODS A. Measurement & Protocol Surface EMG signals from a pair of antagonist elbow muscles (Bsh-biceps short & Tlh-triceps long) of 5 healthy subjects were recorded using GRASS amplifiers. The EMG
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EMG matrix M(t) (size 10 X 200 time-steps) was obtained for each subject and each angle and then used for the synergy extraction process. Angular displacement of the elbow ϴ was estimated using the kinematic data from the recordings. C. Muscle Synergy Extraction Using a 2 stage-customized NMF-algorithm, Temporal Synergies of the form presented in (1), were extracted from the matrix M(t). In this equation the M(t) represents the EMG matrix with the 5 trials pooled together, Ci(t) the synergy wave forms and Ui the activations patterns of each muscle. According to this algorithm, the reconstructed matrices, C and U, were estimated through an iterative process based on the multiplicative update rules and alternating least-squares [12].
Figure 1. Experiment protocol. (A) and (B) Movement trajectory is performed 5 times with starting and final position defined by ϴf and 5 seconds of still position in between. (C) Followed by a muscle synergy extraction, a scaling and a simulation stage.
N
M (t ) U i Ci (t )
waveforms were recorded during moving execution of a point to point trajectory on the horizontal plane at 3 different elbow angles (ϴ = 30°, 48° and 79°). The movement was repeated 5 times per angle and per person according the protocol showed in Fig.1.
The convergence criterion to terminate the iterations was defined by the relative change & residual change of the approximated matrices < 10-5. Note. Data processing and Synergy Extraction were done with scripts written in Matlab (Mathworks, Natick,MA).
The data recordings on healthy humans of this study were approved by the Internal Review Board (IRB) of University of Southern California (USC).
D. Virtual Model of the Arm The Virtual Arm (VA) model, developed by Song et al [11, 13], was used to assess/evaluate the potential of a synergy-based strategy to reproduce and control the elbow single joint movement. The model presented on the third block of Fig.2, utilizes 3 pairs of antagonist muscles deltoid posterior (DP) & pectoralis major (PC), brachialis (BS) & triceps lateral (Tlt) and biceps short (Bsh) & triceps long (Tlh). However, for this study, only single-joint movement of the elbow comprising one pair of muscles Bsh and Tlh was analyzed.
B. Data Processing The EMG-signals were first processed offline using the standard methodology including filtering out unwanted power line noise at 50Hz and harmonics, band pass filtered between 20-450 Hz to remove motion artifacts & high frequency noise, full-wave rectified and finally low pass filtered at 20 Hz. The EMG-recordings corresponding to each angle ϴ were organized in a matrix array where the 5 corresponding trials were concatenated vertically. The obtained data set array were then normalized in time to 200 steps and in amplitude respect muscle, subject and trial. An
Synergy Extraction
Alpha Input A
B
C
(1)
i 1
D α-Bsh
VA-Model
Joint Kinematics
α-Tlh
Joint Angle Angular Velocity Scaled Weight Factor w
Figure 2. Outline of muscle synergy-based strategy. First block (left), muscle synergy extraction of 2 muscles Bsh and Tlh. Second Block, input parameters of VA-model, alpha input signals & scaled weight factor w. Third block, VA-model with alpha inputs customized for single-joint motor task. Fourth block, joint kinematics output, joint angle & angular velocity.
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elbow movement. The weight factor presented in the alpha input block of Fig.2 was used as an input for the VA model. In this study, general analysis was carried out for 5 subjects, however the simulation using the VA model was only carried out based on the data of one subject. III. RESULTS A. Muscle Synergy Analysis The NMF algorithm implemented showed a reliable approximation of matrices U and C for the pair of elbow muscles Bsh and Tlh. In the first block of Fig.2 the EMGdata of subject 1 corresponding to angular elbow displacement of ϴ=30° confirm how the linear combination of 2 temporal muscle synergies (C factor) and their corresponding activation patterns (U factor), highlighted in different colors, can reproduce a reliable reconstructed signal. The synergy 2 in blue was most probably related with acceleration of elbow extension with high activation coefficient of the triceps and lower activation of biceps. The synergy 1 in red was related with deceleration and postural
Figure 3. Elbow angle displacement obtained from the simulation for different weight factors w. Values of the w from 0.3 to 1.3 can produce angles ϴ from 0° to 90°.
Figure 4. The scaled weight factor w and the corresponding angles of the elbow movement were fitted to a quadratic curve showing the capability to reproduce elbow movement from close to 0° to 90°.
E. VA Simulation & Input Settings The general input settings used for the simulation included a 20 seconds alpha input signal composed of 4stages. As it is shown on the second block of Fig.2, the first stage (A) corresponds to 5s of system initialization followed by a second stage (B) of 5s for static control at the initial position, the third stage (C) of less than 1s for the dynamic movement and a final stage (D) of less than 10s for static control after movement termination. In order to simulate the single-joint motor task of the shoulder, all the muscles were fixed with the exception of Bsh and Tlh. The dynamic movement stage (C) was built from the EMG reconstructed-signals resulting from the muscle synergies extraction process (product UC). In the first block of Fig.2, the reconstructed signal is depicted in black trace. Different elbow angles were obtained in the simulation by choosing a suitable scaling factor. The scaling factor, called weight factor w in this study, was a value range from 0.3-1.3 obtained based on the alpha dynamic parameters and activation patterns from the synergy extraction process that produced the closest elbow angle compared with the real
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Figure 5. The results of the simulations using the VA model (dotted line) compared with the real elbow movements (solid line) at different angles ϴ.
Figure 6. Angular velocity profiles of the elbow. In black trace the original movement and in colors the simulated velocities with different values of the weight factor w.
control at the final position. B. VA Simulation & Control Performance The results obtained from the movement simulations done in the VA model displayed in general a good capability of reproduce different angular elbow movements by changing the scaled weight factor w. From Fig.3 it is possible to observe the simulated elbow movement in the time frame of 0.4 seconds. In this case a change on the weight factor with values from 0.3 to 1.3, produced different elbow angles ranging from 100 to 900. For the scaling value estimated for this subject, changes of 0.1 on the weight factor w produced angle displacements of 5° to 15° approximately. A quadratic polynomial curve shown in Fig. 4 was used to fit the elbow angle and weight factor data obtained from the simulation. The R2 value of 0.9 was obtained for the fitting, indicating generally good performance of this strategy to reproduce a class of elbow movements in the range of 0° to 90°.
Future direction of this study is to extend this approach to multi-muscle/multi-joint NMES including an adaptive control algorithm [15]. Such adaptive algorithm may provide a more efficient way to tune muscle activation pattern in trial-to-trial control of motor performance. Once the synergybased control strategy is validated with simulation studies, clinical test of this control strategy for stroke rehabilitation can be performed. REFERENCES [1]
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The results depicted in Fig.5 illustrate the close resemblance between original and simulated elbow movements. In this figure original and simulated angular displacement is presented for different elbow angles ϴ. In a similar way the elbow velocity curves presented in Fig.6 showed also closeness in shape. The velocity bell-shape of natural and simulated curves shows visible differences at higher values of the weight factor.
[5]
[6] [7] [8]
IV. DISCUSSION & CONCLUSION The goal of this study is to present a proof-of-concept of a synergy-based strategy of NMES control for stroke rehabilitation. Up till now, muscle synergy analysis has been applied in revealing the modular nature of motor programming and execution [6], as well as in evaluating the motor function losses and rehabilitation effects in post-stroke patients [5]. However, there haven’t been any applications of muscle synergies in NMES controller design. In NMES application of upper extremity rehabilitation, one of the major obstacles to move on to extensive clinical practice was the complexity of planning individualized control commands for each of the multiple muscles involved [14]. The muscle synergy analysis of normal movement patterns provides a plausible framework for resolving the redundant muscle control problem. This study unveils a reliable strategy of how a simply scaling of a synergy activation pattern is able to reproduce different single joint elbow movements. The results obtained in single joint movement control provide evidence of the potentiality to extend this concept to a multi-joint approach and hence a muscle synergy-based strategy for NMES control in general.
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