Human Activity Recognition using Time Series Feature Extraction and Active Learning

Today, portable devices like smartwatches and smartphones have made a great impact in human's wellbeing. From sleep monitoring to exercise scheduling, Human Activity Recognition had played a major role in the habits of the people. In this work, we exploit a Time Series dataset that describes a Human Activity Recognition signal. In the beginning, we extract the features oriented on Spectral, Statistical and Temporal domains. Then, we construct a dataset for each domain and we calculate the classification results using a number of different classifiers. In the sequel, we apply Active Learning techniques and calculate their classification accuracy performance using a small portion of the original datasets as initial labeled set. Finally, we compare the original results with the ones produced with Active Learning methods.