scholarly journals The methods of EMG data processing

2017 ◽  
Vol 3 ◽  
pp. 38-45
Author(s):  
Michał Serej ◽  
Maria Skublewska - Paszkowska
Keyword(s):  
Data Processing ◽  
Motion Capture ◽  
Signal Analysis ◽  
Emg Signal ◽  
Capture Technique

The article presents both the methods of data processing of electromyography (EMG), and EMG signal analysis using the implemented piece of software. This application is used to load the EMG signal stored in a file with the .C3D extension. The analysis was conducted in terms of the highest muscles activaton during exercise recorded with Motion Capture technique.

Development of Basketball Assisted Training System Based on Kinect Motion Capture

2014 ◽  
Vol 926-930 ◽  
pp. 1318-1321
Author(s):  
Teng Da Li ◽  
Fang Wen
Keyword(s):  
Data Processing ◽  
Motion Capture ◽  
Three Dimensional ◽  
Training System ◽  
Motion Sensor ◽  
Technology Application ◽  
Motion Data ◽  
Time Performance ◽  
Object Technology ◽  
Capture Technique

Motion capture technique is motion data processed by a motion sensor and optical equipment tracker record computer. Three-dimensional information is to restore the moving object technology. Application of motion capture technology in sports training, the training into the scientific digital stage update the motion capture technology make it cheaper. The paper studied the technology of Kinect based motion capture, and its application in basketball training. This method has the advantages of simple data processing, high real-time performance and low price etc..

Operative Ontologien – ein Versuch, einen klaren Begriff zu verunreinigen

2017 ◽  
Vol 8 (2) ◽  
pp. 187-192
Author(s):  
Gertrud Koch
Keyword(s):  
Motion Capture ◽  
Human Being ◽  
Visual Effects ◽  
Human Environment ◽  
Ontological Question ◽  
Media And Technology ◽  
Live Action ◽  
Capture Technique

"Operative Ontologien werden in diesem Artikel als relationale kommunikative Situationen vorgestellt, in denen Medien und Technik Teil einer Praxis sind, aber nicht einfach mit dieser zusammenfallen. Die Ontologie bezieht sich auf eine temporäre Konstellation, beispielsweise eine Verknüpfung von Maschine, Körper und Bild, in der die ontologische Frage der Anthropologie perspektivisch immer wieder verschoben wird. Wie das genau zu verstehen ist, wird am Fallbeispiel der Motion-Capture-Technik deutlich, in der durch eine Verschmelzung von Live Action Movie und der animierten Welt der Visual Effects eine permanente Veränderung dessen erfolgt, was als Mensch oder menschliche Umwelt angesehen wird. This article presents operational ontologies as communicative situations in which media and technology are part of a practice, but do not simply coincide with it. Ontology refers to a temporary constellation, for example a link between machine, body and image, which shifts the ontological question of anthropology in perspective time and again. This thesis is further illustrated by a case study of the motion capture technique, whose merging of live action movie and the animated world of visual effects leads to a permanent modification of our notions of the human being and human environment. "

scholarly journals Screening of sleep movement disorders via EMG signal analysis

2021 ◽  
Author(s):  
Mehrnaz Shokrollahi
Keyword(s):  
Feature Extraction ◽  
Sparse Representation ◽  
Movement Disorders ◽  
Dictionary Learning ◽  
Signal Analysis ◽  
Compact Set ◽  
Emg Signal ◽  
Increased Risk ◽  
Non Stationary Signal ◽  
Representation Techniques

It is estimated that 50 to 70 million Americans suffer from a chronic sleep disorder, which hinders their daily life, affects their health, and incurs a significant economic burden to society. Untreated Periodic Leg Movement (PLM) or Rapid Eye Movement Behaviour Disorder (RBD) could lead to a three to four-fold increased risk of stroke and Parkinson’s disease respectively. These risks bring about the need for less costly and more available diagnostic tools that will have great potential for detection and prevention. The goal of this study is to investigate the potentially clinically relevant but under-explored relationship of the sleep-related movement disorders of PLMs and RBD with cerebrovascular diseases. Our objective is to introduce a unique and efficient way of performing non-stationary signal analysis using sparse representation techniques. To fulfill this objective, at first, we develop a novel algorithm for Electromyogram (EMG) signals in sleep based on sparse representation, and we use a generalized method based on Leave-One-Out (LOO) to perform classification for small size datasets. In the second objective, due to the long-length of these EMG signals, the need for feature extraction algorithms that can localize to events of interest increases. To fulfill this objective, we propose to use the Non-Negative Matrix Factorization (NMF) algorithm by means of sparsity and dictionary learning. This allows us to represent a variety of EMG phenomena efficiently using a very compact set of spectrum bases. Yet EMG signals pose severe challenges in terms of the analysis and extraction of discriminant features. To achieve a balance between robustness and classification performance, we aim to exploit deep learning and study the discriminant features of the EMG signals by means of dictionary learning, kernels, and sparse representation for classification. The classification performances that were achieved for detection of RBD and PLM by means of implicating these properties were 90% and 97% respectively. The theoretical properties of the proposed approaches pertaining to pattern recognition and detection are examined in this dissertation. The multi-layer feature extraction provide strong and successful characterization and classification for the EMG non-stationary signals and the proposed sparse representation techniques facilitate the adaptation to EMG signal quantification in automating the identification process.

A non-parametric statistical approach to EMG signal analysis

2003 ◽  
Author(s):  
R.F. Erlandson ◽  
R.L. Joynt ◽  
S.J. Wu ◽  
C.-M. Wang
Keyword(s):  
Signal Analysis ◽  
Statistical Approach ◽  
Emg Signal ◽  
Non Parametric

scholarly journals Emg Signal Analysis of Healthy and Neuropathic Individuals

2017 ◽  
Vol 225 ◽  
pp. 012128
Author(s):  
Ashutosh Gupta ◽  
Tabassum Sayed ◽  
Ridhi Garg ◽  
Richa Shreyam
Keyword(s):  
Signal Analysis ◽  
Emg Signal
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