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

Abstract

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. 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.