This paper focuses on addressing the problem of data scarcity for gait analysis. Standard augmentation methods may produce gait sequences that are not consistent with the biomechanical constraints of human walking. To address this issue, we propose a novel framework for gait data augmentation by using OpenSIM, a physics-based simulator, to synthesize biomechanically plausible walking sequences. The proposed approach is validated by augmenting the WBDS and CASIA-B datasets and then training gait-based classifiers for 3D gender gait classification and 2D gait person identification respectively. Experimental results indicate that our augmentation approach can improve the performance of model-based gait classifiers and deliver state-of-the-art results for gait-based person identification with an accuracy of up to 96.11% on the CASIA-B dataset.
School of information Science and Engineering, Lanzhou University（兰州大学信息科学与工程学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Institute of Engineering Medicine, Beijing Institute of Technology（北京理工大学医学工程学院）
In recent years, the incidence of depression is rising rapidly worldwide, but large-scale depression screening is still challenging. Gait analysis provides a non-contact, low-cost, and efficient early screening method for depression. However, the early screening of depression based on gait analysis lacks sufficient effective sample data. In this paper, we propose a skeleton data augmentation method for assessing the risk of depression. First, we propose five techniques to augment skeleton data and apply them to depression and emotion datasets. Then, we divide augmentation methods into two type…
There are several confounding factors that can reduce the accuracy of gait recognition systems. These factors can reduce the distinctiveness, or alter the features used to characterise gait, they include variations in clothing, lighting, pose and environment, such as the walking surface. Full invariance to all confounding factors is challenging in the absence of high-quality labelled training data. We introduce a simulation-based methodology and a subject-specific dataset which can be used for generating synthetic video frames and sequences for data augmentation. With this methodology, we gene…
Imperial College London（伦敦帝国理工学院）；Shanghai Jiao Tong University（上海交通大学）
Accurate estimation of three-dimensional human skeletons from depth images can provide important metrics for healthcare applications, especially for biomechanical gait analysis. However, there exist inherent problems associated with depth images captured from a single view. The collected data is greatly affected by occlusions where only partial surface data can be recorded. Furthermore, depth images of human body exhibit heterogeneous characteristics with viewpoint changes, and the estimated poses under local coordinate systems are expected to go through equivariant rotations. Most existing po…
The Gait Signature of Frailty: Transfer Learning based Deep Gait Models for Scalable Frailty Assessment
Department of Electrical and Computer Engineering（电气工程系）；Johns Hopkins University（约翰霍普金斯大学）；Division of Geriatrics and Gerontology Medicine（老年医学科）；Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）；Department of Computer Science（计算机科学系）；Center for Movement Studies（运动研究中心）；Kennedy Krieger Institute（肯尼迪-克里格尔研究所）；Department of Physical Medicine and Rehabilitation（康复医学系）；Department of Biomedical Engineering（生物医学工程系）
A Multi-View High-Resolution Foot-Ankle Complex Point Cloud Dataset During Gait for Occlusion-Robust 3D Completion
College of Biomedical Engineering（生物医学工程学院）；College of Computer Science and Artificial Intelligence（计算机科学与人工智能学院）；Department of Information Engineering（信息工程系）；School of Computer Science and Engineering（计算机科学与工程学院）
GAITEX: Human motion dataset of impaired gait and rehabilitation exercises using inertial and optical sensors
AI for Sensor Data Analytics Research Group（人工智能传感器数据解析研究组）；Ulm University of Applied Sciences（乌尔姆应用科学大学）；Biomechatronic Research Group（生物机械研究组）；Institute of Computer Science（计算机科学研究所）
Beijing Jiaotong University（北京交通大学）；PhiGent Robotics（鉴智机器人）；National University of Singapore（新加坡国立大学）；NetEase Fuxi AI Lab（网易伏羲人工智能实验室）；University of Technology Sydney（悉尼科技大学）
Gait recognition is a biometric technology that recognizes the identity of humans through their walking patterns. Compared with other biometric technologies, gait recognition is more difficult to disguise and can be applied to the condition of long-distance without the cooperation of subjects. Thus, it has unique potential and wide application for crime prevention and social security. At present, most gait recognition methods directly extract features from the video frames to establish representations. However, these architectures learn representations from different features equally but do no…
OmniMimic: Dynamics-completed Motion Augmentation for Multi-style Omnidirectional Quadruped Locomotion
Hunan University（湖南大学）；China Mobile Group Hunan Company Ltd.（中国移动通信集团湖南有限公司）；National Engineering Research Center of Robot Visual Perception and Control Technology（国家机器人视觉感知与控制技术工程研究中心）
King’s College London（伦敦国王学院）；Indiana University（印第安纳大学）；University College London（伦敦大学学院）
We present a proof-of-concept, deep learning (DL) based, differentiable biomechanical model of realistic brain deformations. Using prescribed maps of local atrophy and growth as input, the network learns to deform images according to a Neo-Hookean model of tissue deformation. The tool is validated using longitudinal brain atrophy data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and we demonstrate that the trained model is capable of rapidly simulating new brain deformations with minimal residuals. This method has the potential to be used in data augmentation or for the…
3D Gait-Based Autism Classification Using Attention-Enhanced Deep Learning with Cross-Fold Statistical Stability Analysis
Begum Rokeya University（贝古姆罗基亚大学）；Texas State University（德克萨斯州立大学）；Asian Institute of Technology（亚洲理工学院）；National Center for Geriatrics and Gerontology（国立长寿医疗研究中心）
Benchmarking Skeleton-based Motion Encoder Models for Clinical Applications: Estimating Parkinson's Disease Severity in Walking Sequences
University of Toronto（多伦多大学）；Toronto Rehabilitation Institute – University Health Network（多伦多康复研究所 - 大学健康网络）；Vienna University of Technology（维也纳工业大学）；Center for Mental Health – University Health Network（心理健康中心 - 大学健康网络）
TOC Biometrics（TOC生物识别公司）；Pontificia Universidad Catolica del Ecuador（厄瓜多尔天主教大学）
Biometrics is the science of identifying an individual based on their intrinsic anatomical or behavioural characteristics, such as fingerprints, face, iris, gait, and voice. Iris recognition is one of the most successful methods because it exploits the rich texture of the human iris, which is unique even for twins and does not degrade with age. Modern approaches to iris recognition utilize deep learning to segment the valid portion of the iris from the rest of the eye, so it can then be encoded, stored and compared. This paper aims to improve the accuracy of iris semantic segmentation systems …
