Abstract
Human activity recognition is an emerging field of ubiquitous and pervasive computing. Although recent smartphones have powerful resources, the execution of machine learning algorithms on a large amount of data is still a burden on smartphones. Three major factors including; classification algorithm, data feature, and smartphone position influence the recognition accuracy and time. In this paper, we present a comparative study of six classification algorithms, six data features, and four different positions that are most commonly used in the recognition process using smartphone accelerometer. This analysis can be used to select any specific classification algorithm, data feature, and smartphone position for human activity recognition in terms of accuracy and response time. The methodology we used is composed of two major components; a data collector, and a classifier. A set of eleven activities of daily living, four different positions for data collection and ten volunteers contributed to make it a worth-full comparative study. Results show that K-Nearest Neighbor and J48 algorithms performed well both in terms of time and accuracy irrespective of data features whereas the performance of other algorithms is dependent on the selected data features. Similarly, mean and mode features gave good results in terms of accuracy irrespective of the classification algorithm. A short version of the paper has already been presented at ICIS 2014.
Highlights
Human activity recognition has become an active field of research in the previous few years due to its direct impact on the life of a common man
The right choice of the classification algorithm, data feature, and smartphone position is required for improving the accuracy and timely recognition, which is required for different types of applications
Six classification algorithms that are most commonly used in the process of human activity recognition using smartphone accelerometer including; J48, Naïve Bayes, Bayesian Network, K-Nearest Neighbor, Multilayer Perceptron, and Logistic Regression are selected
Summary
Human activity recognition has become an active field of research in the previous few years due to its direct impact on the life of a common man. The right choice of the classification algorithm, data feature, and smartphone position is required for improving the accuracy and timely recognition, which is required for different types of applications. To achieve these motives, a comparative study is carried out. Six classification algorithms that are most commonly used in the process of human activity recognition using smartphone accelerometer including; J48 (decision tree), Naïve Bayes, Bayesian Network, K-Nearest Neighbor, Multilayer Perceptron, and Logistic Regression are selected. Mean Standard Deviation Correlation Variance Mode Kurtosis application Both are used in human activity recognition process using smartphone accelerometer and have their own merits and limitations.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Networked and Distributed Computing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
