Human activity classification using deep learning based on 3D motion feature
Endang Sri Rahayu, Eko Mulyanto Yuniarno, I Ketut Eddy Purnama, Mauridhi Hery Purnomo
Abstract
Human activity classification is needed to support various fields. The health sector, for example, requires the ability to monitor the activities of patients, the elderly, or people with special needs to provide services with fast response as needed. In the traditional classification model, the steps taken to start from the input of data and then proceed with feature extraction, representation, classifier and end with semantic labels. The classification stage uses Convolutional Neural Network (CNN) deep learning to data input, CNN, and semantic labels. This paper proposes a novel method of classifying nine activities based on the movement features of changes in joint distance using Euclidean on the order of frames in each activity segment as input to the CNN model. This study’s motion feature extraction technique was tested using various window sizes to obtain the best classification accuracy. The experimental results show that the selection of window size 16 on the motion feature setting will produce an optimal model accuracy of 94.08% in classifying human activities.