scholarly journals A Novel Muscle Synergy Extraction Method Used for Motor Function Evaluation of Stroke Patients: A Pilot Study

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3833
Author(s):  
Yehao Ma ◽  
Changcheng Shi ◽  
Jialin Xu ◽  
Sijia Ye ◽  
Huilin Zhou ◽  
...  
Keyword(s):  
Motor Function ◽  
Matrix Factorization ◽  
Extraction Method ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Simulated Data ◽  
Muscle Synergy ◽  
Multivariate Curve Resolution ◽  
Function Evaluation ◽  
Stroke Patients

In this paper, we present a novel muscle synergy extraction method based on multivariate curve resolution–alternating least squares (MCR-ALS) to overcome the limitation of the nonnegative matrix factorization (NMF) method for extracting non-sparse muscle synergy, and we study its potential application for evaluating motor function of stroke survivors. Nonnegative matrix factorization (NMF) is the most widely used method for muscle synergy extraction. However, NMF is susceptible to components’ sparseness and usually provides inferior reliability, which significantly limits the promotion of muscle synergy. In this study, MCR-ALS was employed to extract muscle synergy from electromyography (EMG) data. Its performance was compared with two other matrix factorization algorithms, NMF and self-modeling mixture analysis (SMMA). Simulated data sets were utilized to explore the influences of the sparseness and noise on the extracted synergies. As a result, the synergies estimated by MCR-ALS were the most similar to true synergies as compared with SMMA and NMF. MCR-ALS was used to analyze the muscle synergy characteristics of upper limb movements performed by healthy (n = 11) and stroke (n = 5) subjects. The repeatability and intra-subject consistency were used to evaluate the performance of MCR-ALS. As a result, MCR-ALS provided much higher repeatability and intra-subject consistency as compared with NMF, which were important for the reliability of the motor function evaluation. The stroke subjects had lower intra-subject consistency and seemingly had more synergies as compared with the healthy subjects. Thus, MCR-ALS is a promising muscle synergy analysis method for motor function evaluation of stroke patients.

scholarly journals Motor Function Assessment of Upper Limb in Stroke Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bingyu Pan ◽  
Zhen Huang ◽  
Tingting Jin ◽  
Jiankang Wu ◽  
Zhiqiang Zhang ◽  
...  
Keyword(s):  
Motor Function ◽  
Upper Limb ◽  
Evaluation Method ◽  
Inertial Sensor ◽  
Treatment Strategies ◽  
Nonnegative Matrix ◽  
Sensor Data ◽  
Muscle Synergy ◽  
Control Group ◽  
Stroke Patients

Background. Quantitative assessment of motor function is extremely important for poststroke patients as it can be used to develop personalized treatment strategies. This study aimed to propose an evaluation method for upper limb motor function in stroke patients. Methods. Thirty-four stroke survivors and twenty-five age-matched healthy volunteers as the control group were recruited for this study. Inertial sensor data and surface electromyography (sEMG) signals were collected from the upper limb during voluntary upward reaching. Five features included max shoulder joint angle, peak and average speeds, torso balance calculated from inertial sensor data, and muscle synergy similarity extracted from sEMG data by the nonnegative matrix factorization algorithm. Meanwhile, the Fugl–Meyer score of each patient was graded by professional rehabilitation therapist. Results. Statistically significant differences were observed among severe, mild-to-moderate, and control group of five features ( p   ≤  0.001). The features varied as the level of upper limb motor function changes since these features significantly correlated with the Fugl–Meyer assessment scale ( p   ≤  0.001). Moreover, the Bland–Altman method was conducted and showed high consistency between the evaluation method of five features and Fugl–Meyer scale. Therefore, the five features proposed in this paper can quantitatively evaluate the motor function of stroke patients which is very useful in the rehabilitation process.

scholarly journals Comparison of Initialization Techniques for the Accurate Extraction of Muscle Synergies from Myoelectric Signals via Nonnegative Matrix Factorization

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Mumtaz Hussain Soomro ◽  
Silvia Conforto ◽  
Gaetano Giunta ◽  
Simone Ranaldi ◽  
Cristiano De Marchis
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Simulated Data ◽  
Surface Emg ◽  
Muscle Synergies ◽  
Accurate Identification ◽  
Experimental Surface ◽  
Factorization Algorithms

The main goal of this work was to assess the performance of different initializations of matrix factorization algorithms for an accurate identification of muscle synergies. Currently, nonnegative matrix factorization (NNMF) is the most commonly used method to identify muscle synergies. However, it has been shown that NNMF performance might be affected by different kinds of initialization. The present study aims at optimizing the traditional NNMF initialization for data with partial or complete temporal dependencies. For this purpose, three different initializations are used: random, SVD-based, and sparse. NNMF was used to identify muscle synergies from simulated data as well as from experimental surface EMG signals. Simulated data were generated from synthetic independent and dependent synergy vectors (i.e., shared muscle components), whose activation coefficients were corrupted by simulating controlled degrees of correlation. Similarly, EMG data were artificially modified, making the extracted activation coefficients temporally dependent. By measuring the quality of identification of the original synergies underlying the data, it was possible to compare the performance of different initialization techniques. Simulation results demonstrate that sparse initialization performs significantly better than all other kinds of initialization in reconstructing muscle synergies, regardless of the correlation level in the data.

scholarly journals A Reliable Muscle Synergy Extraction Method based on Multivariate Curve Resolution-Alternating Least Squares

2021 ◽  
Vol 271 ◽  
pp. 03019
Author(s):  
Yehao Ma ◽  
Changcheng Shi ◽  
Dazheng Zhao ◽  
Sijia Ye ◽  
Guokun Zuo
Keyword(s):  
Least Squares ◽  
Extraction Method ◽  
Neurological Diseases ◽  
Nonnegative Matrix ◽  
Alternating Least Squares ◽  
Muscle Synergy ◽  
Multivariate Curve Resolution ◽  
Local Optimum ◽  
Curve Resolution ◽  
Important Approach

Muscle synergy is an important approach to evaluate motor function for patients with neurological diseases. Nonnegative matrix factorization (NMF) is the most widely used muscle synergy extraction method from electromyography (EMG) data. However, NMF usually falls into local optimum and is susceptible to noise, which significantly limit the promotion of muscle synergy. In this paper, a reliable synergy extraction method based on multivariate curve resolution-alternating least squares (MCRALS) was put forward. Its performance was compared with NMF through analyzing the EMG data of upper limb motor. The repeatability and intra-subject consistency were used to evaluate the two methods. As a result, MCR-ALS provided unique resolution result and better repeatability and consistency in contrast to NMF. Thus, the results of this study are of significance for the expansion and application of muscle synergy in medicine.

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