Human-Activity-Recognition
Human activity recognition is a very important tool in health monitoring of patients suffering from various chronic diseases and for professional and recreational sportsmen. In this repository will be presented two ML models that classify data given from Smartwatch Accelerometer of observed persons. Classification consists of ten chosen daily living activities from WISDM database. ML models that are used are Deep Neural Network and Random Forest Classifier. Results of both models are presented and it shows that they have similar results on authentication of persons that were in the training data group and recognition of activities of persons that were used only in the test group of the dataset.
