Muscle Activity Analysis Using Higher-Order Tensor Decomposition: Application to Muscle Synergy Extraction
نویسندگان
چکیده
منابع مشابه
Investigation of Muscle Synergies Using Four Different Methods of Synergy Extraction While Running on a Treadmill in Beginner Runners
Introduction: The study of muscle synergy is a new way to evaluate the functioning of the human body's control system. Different mathematical methods are used to extract muscle synergies from electromyographic data, and this factor can cause different outputs in muscle synergies. Therefore, the aim of this study was to investigate muscle synergies using four different synergy extraction methods...
متن کاملSEMG Signal Processing and Analysis Using Wavelet Transform and Higher Order Statistics to Characterize Muscle Force
An algorithm is proposed for processing and analyzing surface electromyography (SEMG) signals using wavelet transform and Higher Order Statistics (HOS). EMG signal acquires noise while travelling though different media. Wavelet denoising is performed in this research for initial EMG signal processing. With the appropriate choice of the Wavelet Function (WF), it is possible to remove interferenc...
متن کاملElectromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction
Electromyography gives an electrical representation of neuromuscular activation associated with a contracting muscle. The electromyography signal acquires noise while travelling though different media. The wavelet transform is employed for removing noise from surface electromyography (SEMG) and higher order statistics are applied for analysing the signal. With the appropriate choice of wavelet,...
متن کاملGeneralized Higher-Order Tensor Decomposition via Parallel ADMM
Higher-order tensors are becoming prevalent in many scientific areas such as computer vision, social network analysis, data mining and neuroscience. Traditional tensor decomposition approaches face three major challenges: model selecting, gross corruptions and computational efficiency. To address these problems, we first propose a parallel trace norm regularized tensor decomposition method, and...
متن کاملGeneralized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion
Low-rank tensor estimation has been frequently applied in many real-world problems. Despite successful applications, existing Schatten 1-norm minimization (SNM) methods may become very slow or even not applicable for large-scale problems. To address this difficulty, we therefore propose an efficient and scalable core tensor Schatten 1-norm minimization method for simultaneous tensor decompositi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2902122