We focus on the problem of estimating human hand-tremor frequency from input RGB video data. Estimating tremors from video is important for non-invasive monitoring, analyzing and diagnosing patients suffering from motor-disorders such as Parkinson's disease. We consider two approaches for hand-tremor frequency estimation: (a) a Lagrangian approach where we detect the hand at every frame in the video, and estimate the tremor frequency along the trajectory; and (b) an Eulerian approach where we first localize the hand, we subsequently remove the large motion along the movement trajectory of the hand, and we use the video information over time encoded as intensity values or phase information to estimate the tremor frequency.
We estimate hand tremors on a new human tremor dataset, TIM-Tremor, containing static tasks as well as a multitude of more dynamic tasks, involving larger motion of the hands. The dataset has 55 tremor patient recordings together with: associated ground truth accelerometer data from the most affected hand, RGB video data, and aligned depth data.
Tremor is a key diagnostic feature of Parkinson's Disease (PD), Essential Tremor (ET), and other central nervous system (CNS) disorders. Clinicians or trained raters assess tremor severity with TETRAS scores by observing patients. Lacking quantitative measures, inter- or intra- observer variabilities are almost inevitable as the distinction between adjacent tremor scores is subtle. Moreover, clinician assessments also require patient visits, which limits the frequency of disease progress evaluation. Therefore it is beneficial to develop an automated assessment that can be performed remotely an…
Contactless hand tremor amplitude measurement using smartphones: development and pilot evaluation
University of Manchester（曼彻斯特大学）；University of Leeds（利兹大学）；University of Tasmania（塔斯马尼亚大学）；Leeds Teaching Hospitals Trust（利兹教学医院信托）；Loughborough University（拉夫堡大学）
Pose-based Tremor Type and Level Analysis for Parkinson's Disease from Video
Durham University（杜伦大学）；University of Glasgow（格拉斯哥大学）；Newcastle University（纽卡斯尔大学）；National Institute for Health and Care Research Newcastle Biomedical Research Centre（国家健康与护理研究所纽卡斯尔生物医学研究中心）；The Newcastle upon Tyne Hospitals NHS Foundation Trust（泰恩河畔纽卡斯尔国民保健服务基金会信托医院）
Parkinson's disease (PD) is a progressive neurodegenerative disorder that results in a variety of motor dysfunction symptoms, including tremors, bradykinesia, rigidity and postural instability. The diagnosis of PD mainly relies on clinical experience rather than a definite medical test, and the diagnostic accuracy is only about 73-84% since it is challenged by the subjective opinions or experiences of different medical experts. Therefore, an efficient and interpretable automatic PD diagnosis system is valuable for supporting clinicians with more robust diagnostic decision-making. To this end, …
Khoury College of Computer Sciences, Northeastern University（东北大学库里计算机科学学院）；Labrynthe Pvt. Ltd.（Labrynthe私人有限公司）；Center for Perinatal Research, Abigail Wexner Research Institute, Nationwide Children’s Hospital（全国儿童医院阿比盖尔·韦克斯纳研究所围产期研究中心）；Department of Pediatrics, The Ohio State University College of Medicine（俄亥俄州立大学医学院儿科系）
Detecting tremors is challenging for both humans and machines. Infants exposed to opioids during pregnancy often show signs and symptoms of withdrawal after birth, which are easy to miss with the human eye. The constellation of clinical features, termed as Neonatal Abstinence Syndrome (NAS), include tremors, seizures, irritability, etc. The current standard of care uses Finnegan Neonatal Abstinence Syndrome Scoring System (FNASS), based on subjective evaluations. Monitoring with FNASS requires highly skilled nursing staff, making continuous monitoring difficult. In this paper we propose an aut…
Videos captured with hand-held cameras often suffer from a significant amount of blur, mainly caused by the inevitable natural tremor of the photographer's hand. In this work, we present an algorithm that removes blur due to camera shake by combining information in the Fourier domain from nearby frames in a video. The dynamic nature of typical videos with the presence of multiple moving objects and occlusions makes this problem of camera shake removal extremely challenging, in particular when low complexity is needed. Given an input video frame, we first create a consistent registered version …
Our hands reveal important information such as the pulsing of our veins which help us determine the blood pressure, tremors indicative of motor control, or neurodegenerative disorders such as Essential Tremor or Parkinson's disease. The Computerized Classification of Micro-Motions in the hand using waveforms from mobile phone videos is a novel method that uses Eulerian Video Magnification, Skeletonization, Heatmapping, and the kNN machine learning model to detect the micro-motions in the human hand, synthesize their waveforms, and classify these. The pre-processing is achieved by using Euleria…
From Visual Attribution to Clinical Reasoning: Explainable Parkinson's Disease Screening from Hand-Drawn Patterns
Indian Institute of Technology Indore（印度理工学院印多尔分校）；Indian Institute of Technology Patna（印度理工学院巴特那分校）；M. R. Bangur Hospital（M. R. 班古尔医院）；University of Western Australia（西澳大利亚大学）
In this paper, we concern with the problem of how to automatically extract the steps that compose real-life hand activities. This is a key competence towards processing, monitoring and providing video guidance in Mixed Reality systems. We use egocentric vision to observe hand-object interactions in real-world tasks and automatically decompose a video into its constituent steps. Our approach combines hand-object interaction (HOI) detection, object similarity measurement and a finite state machine (FSM) representation to automatically edit videos into steps. We use a combination of Convolutional…
We introduce the novel problem of anticipating a time series of future hand masks from egocentric video. A key challenge is to model the stochasticity of future head motions, which globally impact the head-worn camera video analysis. To this end, we propose a novel deep generative model -- EgoGAN, which uses a 3D Fully Convolutional Network to learn a spatio-temporal video representation for pixel-wise visual anticipation, generates future head motion using Generative Adversarial Network (GAN), and then predicts the future hand masks based on the video representation and the generated future h…
Hand-4DGS: Feed-Forward 3D Gaussian Splatting for 4D Hand Reconstruction from Egocentric Videos
Yonsei University（延世大学）；Electronics and Telecommunications Research Institute（电子电信研究院）；ETH Zurich（苏黎世联邦理工学院）；Microsoft Spatial AI Lab（微软空间AI实验室）；VGG, University of Oxford（VGG，牛津大学）
Open, or non-laparoscopic surgery, represents the vast majority of all operating room procedures, but few tools exist to objectively evaluate these techniques at scale. Current efforts involve human expert-based visual assessment. We leverage advances in computer vision to introduce an automated approach to video analysis of surgical execution. A state-of-the-art convolutional neural network architecture for object detection was used to detect operating hands in open surgery videos. Automated assessment was expanded by combining model predictions with a fast object tracker to enable surgeon-sp…
University of Barcelona（巴塞罗那大学）；Computer Vision Centre（计算机视觉中心）
Recently, there has been a growing interest in analyzing human daily activities from data collected by wearable cameras. Since the hands are involved in a vast set of daily tasks, detecting hands in egocentric images is an important step towards the recognition of a variety of egocentric actions. However, besides extreme illumination changes in egocentric images, hand detection is not a trivial task because of the intrinsic large variability of hand appearance. We propose a hand detector that exploits skin modeling for fast hand proposal generation and Convolutional Neural Networks for hand re…
In this work, an extensive review of literature in the field of gesture recognition carried out along with the implementation of a simple classification system for hand hygiene stages based on deep learning solutions. A subset of robust dataset that consist of handwashing gestures with two hands as well as one-hand gestures such as linear hand movement utilized. A pretrained neural network model, RES Net 50, with image net weights used for the classification of 3 categories: Linear hand movement, rub hands palm to palm and rub hands with fingers interlaced movement. Correct predictions made fo…
Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；PSL Research University（巴黎文理研究大学）；Univ Gustave Eiffel（古斯塔夫·埃菲尔大学）
Our work aims to obtain 3D reconstruction of hands and manipulated objects from monocular videos. Reconstructing hand-object manipulations holds a great potential for robotics and learning from human demonstrations. The supervised learning approach to this problem, however, requires 3D supervision and remains limited to constrained laboratory settings and simulators for which 3D ground truth is available. In this paper we first propose a learning-free fitting approach for hand-object reconstruction which can seamlessly handle two-hand object interactions. Our method relies on cues obtained wit…
When we watch a video, in which human hands manipulate objects, these hands may obscure some parts of those objects. We are willing to make clear how the objects are manipulated by making the image of hands semi-transparent, and showing the complete images of the hands and the object. By carefully choosing a Half-Diminished Reality method, this paper proposes a method that can process the video in real time and verifies that the proposed method works well.
University of Pennsylvania（宾夕法尼亚大学）；Toyota Research Institute（丰田研究所）
Egocentric videos offer fine-grained information for high-fidelity modeling of human behaviors. Hands and interacting objects are one crucial aspect of understanding a viewer's behaviors and intentions. We provide a labeled dataset consisting of 11,243 egocentric images with per-pixel segmentation labels of hands and objects being interacted with during a diverse array of daily activities. Our dataset is the first to label detailed hand-object contact boundaries. We introduce a context-aware compositional data augmentation technique to adapt to out-of-distribution YouTube egocentric video. We …
National Engineering Research Center for Multimedia Software（多媒体软件国家工程研究中心）；Institute of Artificial Intelligence（人工智能研究院）；Nanyang Technological University（南洋理工大学）；Harbin Institute of Technology（哈尔滨工业大学）；Wuhan University（武汉大学）；Hubei Key Laboratory of Multimedia Network Communication Engineering（多媒体网络通信工程湖北省重点实验室）
Spatial-Temporal Video Super-Resolution (ST-VSR) aims to generate super-resolved videos with higher resolution(HR) and higher frame rate (HFR). Quite intuitively, pioneering two-stage based methods complete ST-VSR by directly combining two sub-tasks: Spatial Video Super-Resolution (S-VSR) and Temporal Video Super-Resolution(T-VSR) but ignore the reciprocal relations among them. Specifically, 1) T-VSR to S-VSR: temporal correlations help accurate spatial detail representation with more clues; 2) S-VSR to T-VSR: abundant spatial information contributes to the refinement of temporal prediction. T…
This paper presents a comparison of various deep learning models such as Exception, Resnet-50, and Inception V3 for the classification and prediction of hand hygiene gestures, which were recorded in accordance with the World Health Organization (WHO) guidelines. The dataset consists of six hand hygiene movements in a video format, gathered for 30 participants. The network consists of pre-trained models with image net weights and a modified head of the model. An accuracy of 37% (Xception model), 33% (Inception V3), and 72% (ResNet-50) is achieved in the classification report after the training …
The University of Sydney（悉尼大学）；Zhejiang University（浙江大学）；Image Derivative Inc.（Image Derivative公司）；Shanghai AI Laboratory（上海人工智能实验室）
This paper presents an approach that reconstructs a hand-held object from a monocular video. In contrast to many recent methods that directly predict object geometry by a trained network, the proposed approach does not require any learned prior about the object and is able to recover more accurate and detailed object geometry. The key idea is that the hand motion naturally provides multiple views of the object and the motion can be reliably estimated by a hand pose tracker. Then, the object geometry can be recovered by solving a multi-view reconstruction problem. We devise an implicit neural r…
ForeHOI: Feed-forward 3D Object Reconstruction from Daily Hand-Object Interaction Videos
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）科学与工程学院）；FNii-Shenzhen（FNii-深圳）；Guangdong Provincial Key Laboratory of Future Networks of Intelligence（广东省未来智能网络关键实验室）；SDS, CUHKSZ（CUHKSZ SDS）
Hand gestures are a natural means of interaction in Augmented Reality and Virtual Reality (AR/VR) applications. Recently, there has been an increased focus on removing the dependence of accurate hand gesture recognition on complex sensor setup found in expensive proprietary devices such as the Microsoft HoloLens, Daqri and Meta Glasses. Most such solutions either rely on multi-modal sensor data or deep neural networks that can benefit greatly from abundance of labelled data. Datasets are an integral part of any deep learning based research. They have been the principal reason for the substanti…
University of Michigan（密歇根大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Hands are the central means by which humans manipulate their world and being able to reliably extract hand state information from Internet videos of humans engaged in their hands has the potential to pave the way to systems that can learn from petabytes of video data. This paper proposes steps towards this by inferring a rich representation of hands engaged in interaction method that includes: hand location, side, contact state, and a box around the object in contact. To support this effort, we gather a large-scale dataset of hands in contact with objects consisting of 131 days of footage as w…
In this work we study the use of 3D hand poses to recognize first-person dynamic hand actions interacting with 3D objects. Towards this goal, we collected RGB-D video sequences comprised of more than 100K frames of 45 daily hand action categories, involving 26 different objects in several hand configurations. To obtain hand pose annotations, we used our own mo-cap system that automatically infers the 3D location of each of the 21 joints of a hand model via 6 magnetic sensors and inverse kinematics. Additionally, we recorded the 6D object poses and provide 3D object models for a subset of hand-…
HanDyVQA: A Video QA Benchmark for Fine-Grained Hand-Object Interaction Dynamics
Institute of Industrial Science, The University of Tokyo（东京大学工业科学研究所）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）；Waseda University（早稻田大学）；Visual Geometry Group, University of Oxford（牛津大学视觉几何组）
InfiniHand: Streaming World-Space Hand Motion Estimation from Egocentric Video
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；University of Science and Technology of China（中国科学技术大学）；University of Liverpool（利物浦大学）；Nanyang Technological University（南洋理工大学）；The University of Hong Kong（香港大学）；Zhejiang University（浙江大学）
Fusing uncalibrated IMUs and handheld smartphone video to reconstruct knee kinematics
Center for Bionic Medicine, Shirley Ryan AbilityLab（雪莉·瑞安能力实验室仿生医学中心）；Department of Biomedical Engineering, Northwestern University（西北大学生物医学工程系）；Department of Physical Medicine and Rehabilitation, Northwestern University（西北大学物理医学与康复系）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
When we use video chat, video game, or other video applications, motion-blurred hands often appear. Accurately extracting these hands is very useful for video editing and behavior analysis. However, existing motion-blurred object extraction methods either need user interactions, such as user supplied trimaps and scribbles, or need additional information, such as background images. In this paper, a novel method which can automatically extract the semi-transparent motion-blurred hand just according to the original RGB image is proposed. The proposed method separates the extraction task into two …
We introduce a new pipeline for hand localization and fingertip detection. For RGB images captured from an egocentric vision mobile camera, hand and fingertip detection remains a challenging problem due to factors like background complexity and hand shape variety. To address these issues accurately and robustly, we build a large scale dataset named Ego-Fingertip and propose a bi-level cascaded pipeline of convolutional neural networks, namely, Attention-based Hand Detector as well as Multi-point Fingertip Detector. The proposed method significantly tackles challenges and achieves satisfactoril…
MagicHOI: Leveraging 3D Priors for Accurate Hand-object Reconstruction from Short Monocular Video Clips
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；ETH Zürich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（智能系统马克斯·普朗克研究所）
