Reconhecimento de atividades humanas utilizando redes neurais auto-associativas e dados de smartphone

Abstract

Human Activity Recognition (HAR) is an important challenging research area with many applications in intelligence ambient, healthcare and homeland security systems. HAR is the process whereby a person is monitored through sensors and analyzed to infer the undergoing activities during a period of time. This work presents the development of two systems for the HAR using auto associative neural networks. The activity recognition systems are based on public dataset that has signal from three static postures (standing, sitting, lying) and three dynamic activities (walking, walking downstairs and walking upstairs).The dataset was captured by using accelerometer and gyroscopic sensor of a Smartphone. The features extracted from the time and the acceleration due to body motion were used to the development of the proposed systems. Our experimental results illustrates the effectiveness of the proposed system.