Human Activity Recognition Based on a Modified Capsule Network

Abstract

Human activity recognition (HAR) has attracted considerable research attention in the past decade with the development of wearable sensor technology and deep learning algorithms. However, most of the existing HAR methods ignored the spatial relationship of features, which may lead to recognition errors. In this paper, a novel model based on a modified capsule network (MCN) is proposed to accurately recognize various human activities. This novel model is composed of a convolution block and a capsule block, which can achieve end-to-end intelligent recognition. In the meantime, the spatial information among features is preserved through a dynamic routing process. To validate the effectiveness of the model, a human activity dataset is constructed by placing an inertial measurement unit (IMU) on the calf of the volunteers to collect their activity data in daily life, including walking, jogging, upstairs, downstairs, up-ramps, and down-ramps. The recognition accuracy of this novel approach can reach 96.08%, which performs better than the convolutional neural network (CNN) with an accuracy of 91.62%. In addition, it is evaluated on two public datasets named WISDM and UCI-HAR, and the accuracies achieve 98.21% and 95.28%, respectively, which presents higher accuracy than the reported results obtained from benchmark algorithms like CNN. The experimental results show that the proposed model has better activity detection capability and achieves outstanding performance for HAR.

Ensemble Multifeatured Deep Learning Models and Applications: A Survey

“…Computer vision is a rapidly evolving field that aims to enable machines to interpret and understand still and stereo visual information from the surrounding world [40]. The primary goal of computer vision is to develop algorithms and techniques that can automatically extract meaningful information from images and videos, such as object recognition [41], scene understanding [2], and motion analysis [42]. However, computer vision faces several challenges, such as variations in lighting conditions, occlusions, and complex cluttered backgrounds [43], which make it difficult to achieve accurate and robust results.…”