Investigation of the HD-sEMG probability density function shapes with varying muscle force using data fusion and shape descriptors

• Published in: Computers in biology and medicine Vol. 89; pp. 44 - 58
• Main Authors: Al Harrach, Mariam, Boudaoud, Sofiane, Carriou, Vincent, Laforet, Jeremy, Letocart, Adrien J., Grosset, Jean-François, Marin, Frédéric
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.10.2017; Elsevier Limited; Elsevier
• Subjects: Accuracy; Bioengineering; Classification; Computer Simulation; Configurations; Data integration; Electrodes; Electromyography; Functional statistics; High density surface electromyogram; High order statistics; Humans; Image processing; Life Sciences; Mathematical models; Modeling; Models, Biological; Multisensor fusion; Muscle contraction; Muscle Strength - physiology; Muscle, Skeletal - physiology; Physiological assessment; Physiology; Probability density function; Probability density functions; Sensitivity analysis; Shape distances; Signal Processing, Computer-Assisted; Statistical analysis; Trends; Variation; High density surface electromyogram; Functional statistics; Physiological assessment; Probability density function; High order statistics; Shape distances; Modeling

This work presents an evaluation of the High Density surface Electromyogram (HD-sEMG) Probability Density Function (PDF) shape variation according to contraction level. On that account, using PDF shape descriptors: High Order Statistics (HOS) and Shape Distances (SD), we try to address the absence o...