Classification and analysis of human activities

In recent years, classification of human activities has gained more interest, this paper presents a novel analysis of a multivariate timeseries dataset namely PAMAP2 (physical activity monitoring) using different classification algorithms. The dataset consist of 18 day-to-day activities with 54 attributes that include sitting, standing, lying, ironing, vaccum-cleaning, walking, running, cycling, nordiac walking, playing soccer, rope jumping, car driving, ascending and descending stairs etc. of which first five activities are selected for analysis. The classification is carried out using Weka data mining tool on algorithms namely J48, Naives Bayes and Random Forest. The results proved an accuracy of 100%, 100%, 98% for training set and 19.2% 90.11% 93.2% for test set on J48, Random Forest and Naives Bayes algorithms respectively. Thereby, the overall analysis demonstrates that Random Forest algorithm outperforms better on PAMAP2 (physical activity monitoring) dataset and the activity, vaccum-cleaning results with highest correctly classified instances on Random Forest and Naives Bayes algorithms.