Comparative analysis of HAR datasets using classification algorithms

Computer Science and Information Systems

article

Suvra Nayak¹  Chhabi Rani Panigrahi¹  Bibudhendu Pati¹  Sarmistha Nanda¹  Meng-Yen Hsieh² 

[1] Department of Computer Science, Rama Devi Women’s University;Department of Computer Science & Information Engineering, Providence University

关键词: Machine Learning;    Human Activity Recognition;    WEKA;    Classifier;    Classification algorithms;   

DOI  :  10.2298/CSIS201221043N

学科分类：土木及结构工程学

来源: Computer Science and Information Systems

【 摘 要 】

In the current research and development era, Human Activity Recognition (HAR) plays a vital role in analyzing the movements and activities of a human being. The main objective of HAR is to infer the current behaviour by extracting previous information. Now-a-days, the continuous improvement of living condition of human beings changes human society dramatically. To detect the activities of human beings, various devices, such as smartphones and smart watches, use different types of sensors, such as multi modal sensors, non-video based and video-based sensors, and so on. Among the entire machine learning approaches, tasks in different applications adopt extensively classification techniques, in terms of smart homes by active and assisted living, healthcare, security and surveillance, making decisions, tele-immersion, forecasting weather, official tasks, and prediction of risk analysis in society. In this paper, we perform three classification algorithms, Sequential Minimal Optimization (SMO), Random Forest (RF), and Simple Logistic (SL) with the two HAR datasets, UCI HAR and WISDM, downloaded from the UCI repository. The experiment described in this paper uses the WEKA tool to evaluate performance with the matrices, Kappa statistics, relative absolute error, mean absolute error, ROC Area, and PRC Area by 10-fold cross validation technique. We also provide a comparative analysis of the classification algorithms with the two determined datasets by calculating the accuracy with precision, recall, and F-measure metrics. In the experimental results, all the three algorithms with the UCI HAR datasets achieve nearly the same accuracy of 98%.The RF algorithm with the WISDM dataset has the accuracy of 90.69%,better than the others.

【 授权许可】

CC BY-NC-ND