title: SVM versus MAP on accelerometer data to distinguish among locomotor activities executed at different speeds.
abstract: Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes' classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25-35 yrs.). From each segmented activity epoch, sixteen features were chosen in the frequency and time domain. Dimension reduction was then performed through 2D Sammon's mapping. An Artificial Neural Network (ANN) was trained to mimic Sammon's mapping on the whole dataset. In the Bayes' approach, the two features were then fed to a Bayes' classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier. Bayes' approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes' scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.
authors.affiliation: Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy.
authors.forename: Maurizio
authors.initials: M
authors.lastname: Schmid
authors.affiliation: Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Riganti-Fulginei
authors.affiliation: Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy.
authors.forename: Ivan
authors.initials: I
authors.lastname: Bernabucci
authors.affiliation: Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy.
authors.forename: Antonino
authors.initials: A
authors.lastname: Laudani
authors.affiliation: Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy.
authors.forename: Daniele
authors.initials: D
authors.lastname: Bibbo
authors.affiliation: Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy.
authors.forename: Rossana
authors.initials: R
authors.lastname: Muscillo
authors.affiliation: Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Salvini
authors.affiliation: Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy.
authors.forename: Silvia
authors.initials: S
authors.lastname: Conforto
mesh_terms: D000054:Acceleration; D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D015444:Exercise; D005260:Female; D006801:Humans; D008124:Locomotion; D008297:Male; D008991:Monitoring, Physiologic / Q000295:instrumentation* / Q000379:methods; D009043:Motor Activity; D009068:Movement; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D060388:Support Vector Machine; D016138:Walking; D059015:Wireless Technology