Muscle synergy extraction during arm reaching movements at different speeds

Sabzevari, Vahid R.; Jafari, Amir; Boostani, Reza

doi:10.3233/thc-161256

Cited by 7 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reaching is one of the effective skilled movements in human daily lives, and it needs to coordinate many muscles acting on related joints [ 48 ]. It is important to understand the motor control strategy and quantify the motor impairment level in reaching movement.…”

Section: Discussionmentioning

confidence: 99%

Motor Function Assessment of Upper Limb in Stroke Patients

Pan

Huang

Jin

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Motor Function Assessment of Upper Limb in Stroke Patients

Pan

Huang

Jin

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…NMF is the most widely used muscle synergy extraction method [ 15 , 16 , 17 , 18 , 19 , 20 , 21 ], which is usually based on the multiplicative update rules [ 36 ]. After creation of the random initial matrices ( C and S ), the iteration is to minimize the Frobenius norm of the residual matrix (preprocessed EMG matrix D minus multiplication of the matrix S and C ) illustrated by Equation (4).…”

Section: Methodsmentioning

confidence: 99%

“…Various matrix decomposition algorithms have been applied to extract muscle synergy from recorded and processed electromyographic (EMG) signals of dynamic motor tasks. Nonnegative matrix factorization (NMF) [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], factor analysis (FA) [ 22 , 23 ], independent component analysis (ICA) [ 24 , 25 ], and principal component analysis (PCA) [ 26 , 27 ] are the four common synergy extraction algorithms. PCA produces the basis vectors (muscle synergies) with the best variance description of EMG data through singular value decomposition (SVD) [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…ICA is employed to analyze the data with non-Gaussian variation and extract synergies that maximize the absolute value of the fourth moment of the data. In contrast to the aforementioned methods, NMF is the most widely used algorithm because of its non-negativity constraint and simple principle [ 6 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 28 ]. However, NMF has some limitations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Shi

et al. 2021

Sensors

View full text Add to dashboard Cite

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.

show abstract