/Human-Activity-Recognition-from-Accelerometer-Data-using-Ensemble-Learning

This project aims to classify the human activities using ensemble learning method. In this project, we compared the recognition accuracy among different classifiers, visualized the data using seaborn library and t-SNE, and tuned the hyperparameters using grid search and k-fold cross-validation.

Primary LanguagePython

Human-Activity-Recognition-from-Accelerometer-Data-Using-Ensemble-Learning

This project aims to classify the human activities using ensemble learning method.

Dataset:

The human activities dataset contains 5 classes (sitting-down, standing-up, standing, walking, and sitting) collected on 8 hours of activities of 4 healthy subjects. The data set is downloaded from

http://groupware.les.inf.puc-rio.br/har#ixzz4Mt0Teae2

Ugulino, W.; Cardador, D.; Vega, K.; Velloso, E.; Milidiu, R.; Fuks, H. Wearable Computing: Accelerometers' Data Classification of Body Postures and Movements. Proceedings of 21st Brazilian Symposium on Artificial Intelligence. Advances in Artificial Intelligence - SBIA 2012. In: Lecture Notes in Computer Science. , pp. 52-61. Curitiba, PR: Springer Berlin / Heidelberg, 2012. ISBN 978-3-642-34458-9. DOI: 10.1007/978-3-642-34459-6_6.