title: Improving human activity recognition and its application in early stroke diagnosis.
abstract: The development of efficient stroke-detection methods is of significant importance in today's society due to the effects and impact of stroke on health and economy worldwide. This study focuses on Human Activity Recognition (HAR), which is a key component in developing an early stroke-diagnosis tool. An overview of the proposed global approach able to discriminate normal resting from stroke-related paralysis is detailed. The main contributions include an extension of the Genetic Fuzzy Finite State Machine (GFFSM) method and a new hybrid feature selection (FS) algorithm involving Principal Component Analysis (PCA) and a voting scheme putting the cross-validation results together. Experimental results show that the proposed approach is a well-performing HAR tool that can be successfully embedded in devices.
authors: Computer Science Department, University of Oviedo, ETSIMO, Oviedo, Asturias 33005, Spain.
authors: José R
authors: JR
authors: Villar
authors: Silvia
authors: S
authors: González
authors: Javier
authors: J
authors: Sedano
authors: Camelia
authors: C
authors: Chira
authors: Jose M
authors: JM
authors: Trejo-Gabriel-Galan
mesh_terms: D000203:Activities of Daily Living; D000465:Algorithms; D001185:Artificial Intelligence; D042241:Early Diagnosis; D017143:Fuzzy Logic; D006801:Humans; D009043:Motor Activity / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D020521:Stroke / Q000175:diagnosis* / Q000503:physiopathology
keywords: Feature selection; PCA; genetic fuzzy finite state machine; genetic fuzzy systems; stroke