Clinical gait data analysis based on Spatio-Temporal features
Harcourt Butler Technological Institute（哈考特·巴特勒技术学院）；Uttarakhand Open University（北阿坎德邦开放大学）
Rohit Katiyar, Dr. Vinay Kumar Pathak
备注：
Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 7 No. 2, February 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis/
Analysing human gait has found considerable interest in recent computer vision research. So far, however, contributions to this topic exclusively dealt with the tasks of person identification or activity recognition. In this paper, we consider a different application for gait analysis and examine its use as a means of deducing the physical well-being of people. The proposed method is based on transforming the joint motion trajectories using wavelets to extract spatio-temporal features which are then fed as input to a vector quantiser; a self-organising map for classification of walking patterns of individuals with and without pathology. We show that our proposed algorithm is successful in extracting features that successfully discriminate between individuals with and without locomotion impairment.
Gait recognition is an important recognition technology, because gait is not easy to camouflage and does not need cooperation to recognize subjects. However, many existing methods are inadequate in preserving both temporal information and fine-grained information, thus reducing its discrimination. This problem is more serious when the subjects with similar walking postures are identified. In this paper, we try to enhance the discrimination of spatio-temporal gait features from two aspects: effective extraction of spatio-temporal gait features and reasonable refinement of extracted features. Th…
Harbin Engineering University（哈尔滨工程大学）；University of British Columbia Okanagan（不列颠哥伦比亚大学奥肯那根分校）；Harbin Institute of Technology（哈尔滨工业大学）；Beijing Institute of Technology Zhuhai（北京理工大学珠海学院）
The critical goal of gait recognition is to acquire the inter-frame walking habit representation from the gait sequences. The relations between frames, however, have not received adequate attention in comparison to the intra-frame features. In this paper, motivated by optical flow, the bilateral motion-oriented features are proposed, which can allow the classic convolutional structure to have the capability to directly portray gait movement patterns at the feature level. Based on such features, we develop a set of multi-scale temporal representations that force the motion context to be richly …
1 Shanghai Key Lab of Intelligent Information Processing, School of Computer Science 2 Institute of Science
Gait recognition plays a vital role in human identification since gait is a unique biometric feature that can be perceived at a distance. Although existing gait recognition methods can learn gait features from gait sequences in different ways, the performance of gait recognition suffers from insufficient labeled data, especially in some practical scenarios associated with short gait sequences or various clothing styles. It is unpractical to label the numerous gait data. In this work, we propose a self-supervised gait recognition method, termed SelfGait, which takes advantage of the massive, di…
Shirley Ryan AbilityLab（雪莉·瑞安能力实验室）；Northwestern University（西北大学）；Baylor College of Medicine（贝勒医学院）
Human pose estimation from monocular video is a rapidly advancing field that offers great promise to human movement science and rehabilitation. This potential is tempered by the smaller body of work ensuring the outputs are clinically meaningful and properly calibrated. Gait analysis, typically performed in a dedicated lab, produces precise measurements including kinematics and step timing. Using over 7000 monocular video from an instrumented gait analysis lab, we trained a neural network to map 3D joint trajectories and the height of individuals onto interpretable biomechanical outputs includ…
Hebei Key Laboratory for Electromagnetic Environmental Effects and Information Processing（河北省电磁环境效应与信息处理重点实验室）；Shijiazhuang Tiedao University（石家庄铁道大学）
Gait recognition, which can realize long-distance and contactless identification, is an important biometric technology. Recent gait recognition methods focus on learning the pattern of human movement or appearance during walking, and construct the corresponding spatio-temporal representations. However, different individuals have their own laws of movement patterns, simple spatial-temporal features are difficult to describe changes in motion of human parts, especially when confounding variables such as clothing and carrying are included, thus distinguishability of features is reduced. In this p…
BioGait-VLM: A Tri-Modal Vision-Language-Biomechanics Framework for Interpretable Clinical Gait Assessment
Department of Computer Science, Drexel University（德克萨斯大学计算机科学系）；Department of Neurological Surgery, Washington University（华盛顿大学神经外科系）；University of California, Berkeley（加州大学伯克利分校）
College of Computer Science and Technology, Qingdao University（青岛大学计算机科学技术学院）；College of Information Science and Engineering, Ocean University of China（中国海洋大学信息科学与工程学院）；School of Informatics, University of Leicester（莱斯特大学信息学院）
It is a challenging task to identify a person based on her/his gait patterns. State-of-the-art approaches rely on the analysis of temporal or spatial characteristics of gait, and gait recognition is usually performed on single modality data (such as images, skeleton joint coordinates, or force signals). Evidence has shown that using multi-modality data is more conducive to gait research. Therefore, we here establish an automated learning system, with an associated spatio-temporal capsule network (ASTCapsNet) trained on multi-sensor datasets, to analyze multimodal information for gait recogniti…
CARE-PD: A Multi-Site Anonymized Clinical Dataset for Parkinson's Disease Gait Assessment
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；KITE Research Institute-UHN（KITE研究 institute-UHN）；University of Strasbourg（斯特拉斯堡大学）；University Hospitals of Strasbourg（斯特拉斯堡大学医院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Federal University of ABC（巴西ABC联邦大学）；KU Leuven（鲁文大学）；Hasselt University（哈瑟尔特大学）；Emory University（埃默里大学）；University of Bristol（布里斯托尔大学）
Clinical-Prior Guided Multi-Modal Learning with Latent Attention Pooling for Gait-Based Scoliosis Screening
Orthopaedic Centre, The University of Hong Kong - Shenzhen Hospital, Shenzhen, China（香港大学深圳医院骨科中心）；Translational Medicine Centre, The University of Hong Kong - Shenzhen Hospital, China（香港大学深圳医院转化医学中心）；Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China（香港大学李嘉成医学院骨科及创伤外科学院）；Huawei, China（华为）
Being able to predict clinical parameters in order to diagnose gait disorders in a patient is of great value in planning treatments. It is known that \textit{decision parameters} such as cadence, step length, and walking speed are critical in the diagnosis of gait disorders in patients. This project aims to predict the decision parameters using two ways and afterwards giving advice on whether a patient needs treatment or not. In one way, we use clinically measured parameters such as Ankle Dorsiflexion, age, walking speed, step length, stride length, weight over height squared (BMI) and etc. to…
Biometric authentication using gait has become a promising field due to its unobtrusive nature. Recent approaches in model-based gait recognition techniques utilize spatio-temporal graphs for the elegant extraction of gait features. However, existing methods often rely on multi-scale operators for extracting long-range relationships among joints resulting in biased weighting. In this paper, we present HEATGait, a gait recognition system that improves the existing multi-scale graph convolution by efficient hop-extraction technique to alleviate the issue. Combined with preprocessing and augmenta…
Parkinson's disease (PD) is a neurological disorder that has a variety of observable motor-related symptoms such as slow movement, tremor, muscular rigidity, and impaired posture. PD is typically diagnosed by evaluating the severity of motor impairments according to scoring systems such as the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Automated severity prediction using video recordings of individuals provides a promising route for non-intrusive monitoring of motor impairments. However, the limited size of PD gait data hinders model ability and clinical po…
Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview Markerless Motion Capture
Shriners Children’s Lexington（夏皮尔儿童医院莱克星顿分院）；Dept. of Physical Therapy, University of Kentucky（肯塔基大学物理治疗系）；Shirley Ryan AbilityLab（希拉里·瑞安能力实验室）；Northwestern University（西北大学）；University of Göttingen（哥廷根大学）；Lower Saxony Center for AI & Causal Methods in Medicine（下萨克森人工智能与因果方法医学中心）；Shriners Children’s Philadelphia（夏皮尔儿童医院费城分院）
St. Pölten Univ. of Applied Sciences（圣珀尔滕应用科学大学）；Orthopaedic Hospital Vienna-Speising（维也纳-施派辛骨科医院）
Three-dimensional clinical gait analysis is essential for selecting optimal treatment interventions for patients with cerebral palsy (CP), but generates a large amount of time series data. For the automated analysis of these data, machine learning approaches yield promising results. However, due to their black-box nature, such approaches are often mistrusted by clinicians. We propose gaitXplorer, a visual analytics approach for the classification of CP-related gait patterns that integrates Grad-CAM, a well-established explainable artificial intelligence algorithm, for explanations of machine l…
CGTGait: Collaborative Graph and Transformer for Gait Emotion Recognition
Huazhong University of Science and Technology, China（华中科技大学）；Wuhan University of Technology, China（武汉理工大学）；Hefei University of Technology, China（合肥工业大学）；Boston University, United States（波士顿大学）
Danang University of Technology and Science（岘港科技大学）；Aix-Marseille Université（艾克斯-马赛大学）；University of Montréal（蒙特利尔大学）
Gait analysis is an important aspect of clinical investigation for detecting neurological and musculoskeletal disorders and assessing the global health of a patient. In this paper we propose to focus our attention on extracting relevant curvature information from the body surface provided by a depth camera. We assumed that the 3D mesh was made available in a previous step and demonstrated how curvature maps could be useful to assess asymmetric anomalies with two simple simulated abnormal gaits compared with a normal one. This research set the grounds for the future development of a curvature-b…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；PediaMed AI；Shenzhen Children’s Hospital（深圳市儿童医院）；Hong Kong Polytechnic University（香港理工大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Gait recognition i.e. identification of an individual from his/her walking pattern is an emerging field. While existing gait recognition techniques perform satisfactorily in normal walking conditions, there performance tend to suffer drastically with variations in clothing and carrying conditions. In this work, we propose a novel covariate cognizant framework to deal with the presence of such covariates. We describe gait motion by forming a single 2D spatio-temporal template from video sequence, called Average Energy Silhouette image (AESI). Zernike moment invariants (ZMIs) are then computed t…
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）
3rd Place Solution to Human Motion Challenges in Real-World and Clinical Settings (MoCha) @ECCV2026: Language-Aligned Motion Representations for Domain-Generalizable UPDRS-Gait Severity Estimation
UNIST（蔚山国立科学技术院）
本文提出语言对齐的运动表示方法，结合 Bi-GRU、文本对齐训练、领域自适应与参数合并，实现领域泛化的 UPDRS-步态严重程度评估，在 MoCha 挑战赛中以 637K 参数取得宏 F1 0.57，排名第三。
Gait, as one of unique biometric features, has the advantage of being recognized from a long distance away, can be widely used in public security. Considering 3D pose estimation is more challenging than 2D pose estimation in practice , we research on using 2D joints to recognize gait in this paper, and a new model-based gait recognition method JointsGait is put forward to extract gait information from 2D human body joints. Appearance-based gait recognition algorithms are prevalent before. However, appearance features suffer from external factors which can cause drastic appearance variations, e…
Quantifying Rodda and Graham Gait Classification from 3D Markerless Kinematics derived from a Single-view Video in a Heterogeneous Pediatric Clinical Cohort
Department of Biomedical Informatics, Emory University（埃默里大学生物医学信息学系）；Shriners Children’s（夏皮罗儿童医院）；The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology（埃默里大学和佐治亚理工学院的沃克·H·库勒生物医学工程系）
Pose2Gait: Extracting Gait Features from Monocular Video of Individuals with Dementia
University of Toronto（多伦多大学）；KITE Research Institute, Toronto Rehabilitation Institute, University Health Network（多伦多康复研究所KITE研究所，大学健康网络）；The Hospital for Sick Children（病童医院）；Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network（多伦多西部医院莫顿与格洛丽亚·舒尔曼运动障碍诊所，大学健康网络）；Krembil Brain Institute, University Health Network（大学健康网络克雷姆比尔脑研究所）；Center for Advancing Neurotechnological Innovation to Application（神经技术创新应用推进中心）；Centre for Mental Health, University Health Network（大学健康网络心理健康中心）
Human gait is one of important biometric characteristics for human identification at a distance. In practice, occlusion usually occurs and seriously affects accuracy of gait recognition. However, there is no available database to support in-depth research of this problem, and state-of-arts gait recognition methods have not paid enough attention to it, thus this paper focuses on gait recognition under occlusion. We collect a new gait recognition database called OG RGB+D database, which breaks through the limitation of other gait databases and includes multimodal gait data of various occlusions …
Single-Stream Multi-Feature Fusion with Temporal Robustness for Gait Emotion Recognition
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；College of Physical Science and Technology, Southwest University（西南大学物理科学与技术学院）；Wisesoft Inc.（四川川大智胜软件股份有限公司）
DAGait: Generalized Skeleton-Guided Data Alignment for Gait Recognition
The Shenzhen International Graduate School, Tsinghua University Shenzhen, China（深圳国际研究生院，清华大学深圳学院中国）；School of Computer Science（计算机科学学院）；Information Engineering, Hefei University of Technology Hefei, China（信息工程，合肥工业大学合肥中国）
School of Cyber Science and Engineering, Shanghai Jiao Tong University（上海交通大学网络空间安全学院）
As an emerging biological identification technology, vision-based gait identification is an important research content in biometrics. Most existing gait identification methods extract features from gait videos and identify a probe sample by a query in the gallery. However, video data contains redundant information and can be easily influenced by bagging (BG) and clothing (CL). Since human body skeletons convey essential information about human gaits, a skeleton-based gait identification network is proposed in our project. First, extract skeleton sequences from the video and map them into a gai…
Harbin Engineering University（哈尔滨工程大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
As a unique and promising biometric, video-based gait recognition has broad applications. The key step of this methodology is to learn the walking pattern of individuals, which, however, often suffers challenges to extract the behavioral feature from a sequence directly. Most existing methods just focus on either the appearance or the motion pattern. To overcome these limitations, we propose a sequential convolutional network (SCN) from a novel perspective, where spatiotemporal features can be learned by a basic convolutional backbone. In SCN, behavioral information extractors (BIE) are constr…
A non-invasive video-based method for individual identification of wildlife using gait dynamics
Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Wildlife Conservation Research Unit, Department of Biology, University of Oxford（牛津大学生物系野生动物保护研究组）
DINOv2 Driven Gait Representation Learning for Video-Based Visible-Infrared Person Re-identification
Kunming University of Science and Technology（昆明理工大学）；Chongqing University of Post and Telecommunications（重庆邮电大学）；China University of Mining Technology（中国矿业大学）；Nanjing University of Science and Technology（南京理工大学）
Toronto Rehabilitation Institute（多伦多康复研究所）；University of Toronto（多伦多大学）；Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）；Sunnybrook Research Institute（桑尼布鲁克研究所）
Because falls are funny, YouTube and other video sharing sites contain a large repository of real-life falls. We propose extracting gait and balance information from these videos to help us better understand some of the factors that contribute to falls. Proof-of-concept is explored in a single video containing multiple (n=14) falls/non-falls in the presence of an unexpected obstacle. The analysis explores: computing spatiotemporal parameters of gait in a video captured from an arbitrary viewpoint; the relationship between parameters of gait from the last few steps before the obstacle and falli…
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）
Detecting Daily Living Gait Amid Huntington's Disease Chorea using a Foundation Deep Learning Model
Tel Aviv University（特拉维夫大学）；Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心）；Teachers College, Columbia University（哥伦比亚大学教师学院）；Wayne State University（韦恩州立大学）；Stony Brook University（石溪大学）；Rush University Medical Center（拉什大学医学中心）
Gait recognition refers to the identification of individuals based on features acquired from their body movement during walking. Despite the recent advances in gait recognition with deep learning, variations in data acquisition and appearance, namely camera angles, subject pose, occlusions, and clothing, are challenging factors that need to be considered for achieving accurate gait recognition systems. In this paper, we propose a network that first learns to extract gait convolutional energy maps (GCEM) from frame-level convolutional features. It then adopts a bidirectional recurrent neural ne…
Universidad Politécnica de Madrid（马德里理工大学）；Centre for Automation and Robotics (CAR UPM-CSIC)（自动化与机器人中心（马德里理工大学-西班牙国家研究委员会））；ETSIDI（马德里理工大学工业设计工程学院）
The use of 3D cameras for gait analysis has been highly questioned due to the low accuracy they have demonstrated in the past. The objective of the study presented in this paper is to improve the accuracy of the estimations made by robot-mounted 3D cameras in human gait analysis by applying a supervised learning stage. The 3D camera was mounted in a mobile robot to obtain a longer walking distance. This study shows an improvement in detection of kinematic gait signals and gait descriptors by post-processing the raw estimations of the camera using artificial neural networks trained with the dat…
