Human physical activity recognition algorithm based on smartphone data and convolutional neural network

Abstract

With a widespread of various sensors embedded in mobile devices, the analysis of human daily activities becomes more common and straightforward. Human activity recognition (HAR) is a prominent application of advanced Machine Learning (ML) and Artificial Intelligence (AI) techniques that utilizes computer vision to understand the semantic meanings of heterogeneous human actions. This paper describes a supervised learning method that can distinguish human actions based on data collected from practical human movements. The primary challenge while working with HAR is to overcome the difficulties that come with the cyclostationary nature of the activity signals. This study proposes a HAR classification model based on a Convolutional Neural Network (CNN) and uses the collected human action signals. The model was tested on the WISDM dataset, which resulted in a 92 % classification accuracy. This approach will help to conduct further researches on the recognition of human activities based on their biomedical signals.