Recognizing human activities using light-weight and effective machine learning methodologies

Abstract

Background Human activity recognition is a dynamic and challenging task. It is a large field of research and development. It involves predicting the movement of a person from the raw sensor data using a machine learning model. To accurately detect human activities for e-health systems, several research attempts have been carried out using data mining and machine learning techniques, but there still is room to improve the performance. To this aim, human activities such as walking, standing, laying, sitting, walking upstairs, walking downstairs are predicted using prominent machine learning models. The aim of human activity recognition is examining actions from photos or video clips. This serves as the driving force behind human activity identification systems' aim to accurately classify input data into the relevant activity category. Methods Six machine learning techniques, including decision tree, random forest, linear regression, Naïve bayes, k-nearest neighbour, and neural networks algorithms, were used for human activity recognition. Results The performance of decision tree, random forest, linear regression, Naïve bayes, k-nearest neighbor, and neural network algorithms was assessed with a human activity recognition dataset. From the results, the random forest classifier and neural network gave good results, whereas the Naïve bayes result was not satisfying. Conclusions We classified the SITTING, STANDING, LAYING, WALKING, WALKING_DOWNSTAIRS, WALKING_UPSTAIRS activities with machine learning techniques with 98% of accuracy