Human-Activity-Recognition-Using-Accelerometer-Data

Human Activity Recognition (HAR) has a wide range of applications due to the widespread usage of acquisition devices such as smartphones and its ability to capture human activity data. The ability to retrieve deeply embedded information for precise detection and its interpretation has been transformed by breakthroughs in Artificial Intelligence (AI). In this paper, the time series dataset, acquired from Wireless Sensor Data Mining Lab (WISDM) Lab, is used to extract features of common human activities from a raw signal data of smartphone accelerometer. A 2D convolutional neural network is used to visualize the data.