We performed an electronic database search of published works from 2012 to mid-2021 that
focus on human gait studies and apply machine learning techniques. We identified six key …

Purpose This paper aims to deal with the human activity recognition using human gait
pattern. The paper has considered the experiment results of seven different activities: normal …

Human action recognition based on skeletons has wide applications in human–computer
interaction and intelligent surveillance. However, view variations and noisy data bring …

Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of
motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected …

Compared to other biometrics, gait is difficult to conceal and has the advantage of being
unobtrusive. Inertial sensors, such as accelerometers and gyroscopes, are often used to …

In this paper, we make two contributions to unsupervised domain adaptation (UDA) using
the convolutional neural network (CNN). First, our approach transfers knowledge in all the …

State-of-the-art methods for 3D hand pose estimation from depth images require large
amounts of annotated training data. We propose modelling the statistical relationship of 3D …

Image segmentation is an important application of polarimetric synthetic aperture radar. This
study aimed to create an 11-layer deep convolutional neural network for this task. The Pauli …

Gait has been considered as a promising and unique biometric for person identification.
Traditionally, gait data are collected using either color sensors, such as a CCD camera …

To improve the security, robustness and imperceptibility of watermark schemes, a novel
watermark scheme is devised by fusing multiple watermark techniques, including lifting …