Integrating Multiple Public Datasets for Human Activity Recognition using Machine Learning.

This study proposes the integration of multiple publicly available datasets for Human Activity Recognition (HAR) to create a unified dataset, which was used to train and test different machine learning algorithms, including J48, kNN, LR, MLP, NB, RF, and SVM. The performance of these algorithms was evaluated in terms of accuracy and F-score, and the results showed that RF achieved the best performance, with an accuracy and F-score of 0.969. The study also compared the results with previous studies on individual datasets and showed that the proposed approach has promising results for HAR.