Amanote Research
Register
Sign In
sEMG Techniques to Detect and Predict Localised Muscle Fatigue
doi 10.5772/25678
Full Text
Open PDF
Abstract
Available in
full text
Date
January 11, 2012
Authors
M. R.
F. Sepulveda
M. Colley
Publisher
InTech
Related search
Detection of Forearm Muscle Fatigue During Piano Playing Using Surface Electromyography (sEMG) Analysis
Genetic Risk Classifier to Predict Localised Renal Cell Carcinoma Recurrence
The Lancet Oncology
Oncology
Energy Conservation Techniques to Decrease Fatigue
Archives of Physical Medicine and Rehabilitation
Physical Therapy
Sports Therapy
Rehabilitation
Sports Science
Muscle Fatigue
Postgraduate Medical Journal
Medicine
A Novel Approach to Predict Fretting Fatigue Crack Initiation
MATEC Web of Conferences
Materials Science
Engineering
Chemistry
Failure to Detect the Novel Retrovirus XMRV in Chronic Fatigue Syndrome
PLoS ONE
Multidisciplinary
Evaluating Subsampling Strategies for sEMG-based Prediction of Voluntary Muscle Contractions
Decision Tree Ensemble Techniques to Predict Thyroid Disease
International Journal of Recent Technology and Engineering
Engineering
Management of Technology
Innovation
Use of Magnetic Flux Techniques to Detect Wheel Tread Damage
Proceedings of the Institution of Civil Engineers: Transport
Civil
Transportation
Structural Engineering