GCHAR: An efficient Group-based Context—aware human activity recognition on smartphone

• Published in: Journal of parallel and distributed computing Vol. 118; no. part-1; pp. 67 - 80
• Main Authors: Cao, Liang, Wang, Yufeng, Zhang, Bo, Jin, Qun, Vasilakos, Athanasios V.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.08.2018
• Subjects: Context awareness; Distribuerade datorsystem; Hierarchical classifier; Human Activity Recognition (HAR); Machine learning; Pervasive Mobile Computing; Hierarchical classifier; Human Activity Recognition (HAR); Context awareness; Machine learning
• ISSN: 0743-7315; 1096-0848; 1096-0848
• DOI: 10.1016/j.jpdc.2017.05.007

With smartphones increasingly becoming ubiquitous and being equipped with various sensors, nowadays, there is a trend towards implementing HAR (Human Activity Recognition) algorithms and applications on smartphones, including health monitoring, self-managing system and fitness tracking. However, one of the main issues of the existing HAR schemes is that the classification accuracy is relatively low, and in order to improve the accuracy, high computation overhead is needed. In this paper, an efficient Group-based Context-aware classification method for human activity recognition on smartphones, GCHAR is proposed, which exploits hierarchical group-based scheme to improve the classification efficiency, and reduces the classification error through context awareness rather than the intensive computation. Specifically, GCHAR designs the two-level hierarchical classification structure, i.e., inter-group and inner-group, and utilizes the previous state and transition logic (so-called context awareness) to detect the transitions among activity groups. In comparison with other popular classifiers such as RandomTree, Bagging, J48, BayesNet, KNN and Decision Table, thorough experiments on the realistic dataset (UCI HAR repository) demonstrate that GCHAR achieves the best classification accuracy, reaching 94.1636%, and time consumption in training stage of GCHAR is four times shorter than the simple Decision Table and is decreased by 72.21% in classification stage in comparison with BayesNet.