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Classification Algorithms in Human Activity Recognition using Smartphones

Year: 2012 Volume: 6 Issue: 8 390 - 397 Pages

Abstract: Rapid advancement in computing technology brings computers and humans to be seamlessly integrated in future. The emergence of smartphone has driven computing era towards ubiquitous and pervasive computing. Recognizing human activity has garnered a lot of interest and has raised significant researches- concerns in identifying contextual information useful to human activity recognition. Not only unobtrusive to users in daily life, smartphone has embedded built-in sensors that capable to sense contextual information of its users supported with wide range capability of network connections. In this paper, we will discuss the classification algorithms used in smartphone-based human activity. Existing technologies pertaining to smartphone-based researches in human activity recognition will be highlighted and discussed. Our paper will also present our findings and opinions to formulate improvement ideas in current researches- trends. Understanding research trends will enable researchers to have clearer research direction and common vision on latest smartphone-based human activity recognition area.

Motor Imagery Signal Classification for a Four State Brain Machine Interface

Year: 2007 Volume: 1 Issue: 5 1304 - 1309 Pages

Abstract: Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification

Analysis of Feature Space for a 2d/3d Vision based Emotion Recognition Method

Year: 2009 Volume: 3 Issue: 11 1976 - 1981 Pages

Abstract: In modern human computer interaction systems (HCI), emotion recognition is becoming an imperative characteristic. The quest for effective and reliable emotion recognition in HCI has resulted in a need for better face detection, feature extraction and classification. In this paper we present results of feature space analysis after briefly explaining our fully automatic vision based emotion recognition method. We demonstrate the compactness of the feature space and show how the 2d/3d based method achieves superior features for the purpose of emotion classification. Also it is exposed that through feature normalization a widely person independent feature space is created. As a consequence, the classifier architecture has only a minor influence on the classification result. This is particularly elucidated with the help of confusion matrices. For this purpose advanced classification algorithms, such as Support Vector Machines and Artificial Neural Networks are employed, as well as the simple k- Nearest Neighbor classifier.

Improving Classification Accuracy with Discretization on Datasets Including Continuous Valued Features

Year: 2011 Volume: 5 Issue: 6 565 - 568 Pages

Abstract: This study analyzes the effect of discretization on classification of datasets including continuous valued features. Six datasets from UCI which containing continuous valued features are discretized with entropy-based discretization method. The performance improvement between the dataset with original features and the dataset with discretized features is compared with k-nearest neighbors, Naive Bayes, C4.5 and CN2 data mining classification algorithms. As the result the classification accuracies of the six datasets are improved averagely by 1.71% to 12.31%.