A CNN-based Method for Human Activity Recognition Using Inertial Measurement Units

Human Activity Recognition using Inertial Measurement Units (IMU) has received particular attention in recent years due to the widespread availability of wearable devices. In this article we present a Convolutional Neural Network-based technique for HAR using IMUs to recognize six human activities. Data from 30 subject was used to validate the system using UCI HAR Dataset. The time series signals were filtered, segmented then transformed into a two-dimensional matrix format, where each row represents a specific time step and each column corresponds to the IMU axes; these data were then fed into the convolutional neural network as input. The results of the experiments demonstrate that the suggested model can effectively capture the spatial and temporal features of IMU data, leading to superior recognition accuracy and relatively lower complexity in comparison to conventional 2-dimensional CNN approaches.