Human activity classification using Decision Tree and Naïve Bayes classifiers

With rapid development in wireless sensor networks and continuous improvements in developing artificial intelligence-based scientific solutions, the concept of ambient assisted living has been encouraged and adopted. This is due to its widespread applications in smart homes and healthcare. In this regard, the concept of human activity recognition (HAR) & classification has drawn numerous researchers’ attention as it improves the quality of life. However, before using this concept in real-time scenarios, it is required to analyse its performance following activities of daily living using benchmarked data set. In this continuation, this work has adopted the activity classification algorithms to improve their accuracy further. These algorithms can be used as a benchmark to analyse others’ performance. Initially, the raw 3-axis accelerometer data is first preprocessed to remove noise and make it feasible for training and classification. For this purpose, the sliding window algorithm, linear and Gaussian filters have been applied to raw data. Then Naïve Bayes (NB) and Decision Tree (DT) classification algorithms are used to classify human activities such as: sitting, standing, walking, sitting down and standing up. From results, it can be seen that maximum 89.5% and 99.9% accuracies are achieved using NB and DT classifiers with Gaussian filter. Furthermore, we have also compared the obtained results with its counterpart algorithms in order to prove its effectiveness.