Hand Pose Estimation with Mems-Ultrasonic Sensors

Hand tracking is an important aspect of human-computer interaction and has a wide range of applications in extended reality devices. However, current hand motion capture methods suffer from various limitations. For instance, visual-based hand pose estimation is susceptible to self-occlusion and changes in lighting conditions, while IMU-based tracking gloves experience significant drift and are not resistant to external magnetic field interference. To address these issues, we propose a novel and low-cost hand-tracking glove that utilizes several MEMS-ultrasonic sensors attached to the fingers, to measure the distance matrix among the sensors. Our lightweight deep network then reconstructs the hand pose from the distance matrix. Our experimental results demonstrate that this approach is both accurate, size-agnostic, and robust to external interference. We also show the design logic for the sensor selection, sensor configurations, circuit diagram, as well as model architecture

A Multi-Modal Sensing Glove for Human Manual-Interaction Studies

We present an integrated sensing glove that combines two of the most visionary wearable sensing technologies to provide both hand posture sensing and tactile pressure sensing in a unique, lightweight, and stretchable device. Namely, hand posture reconstruction employs Knitted Piezoresistive Fabrics that allows us to measure bending. From only five of these sensors (one for each finger) the full hand pose of a 19 degrees of freedom (DOF) hand model is reconstructed leveraging optimal sensor placement and estimation techniques. To this end, we exploit a-priori information of synergistic coordination patterns in grasping tasks. Tactile sensing employs a piezoresistive fabric allowing us to measure normal forces in more than 50 taxels spread over the palmar surface of the glove. We describe both sensing technologies, report on the software integration of both modalities, and describe a preliminary evaluation experiment analyzing hand postures and force patterns during grasping. Results of the reconstruction are promising and encourage us to push further our approach with potential applications in neuroscience, virtual reality, robotics and tele-operation

Wearable haptic systems for the fingertip and the hand: taxonomy, review and perspectives

In the last decade, we have witnessed a drastic change in the form factor of audio and vision technologies, from heavy and grounded machines to lightweight devices that naturally fit our bodies. However, only recently, haptic systems have started to be designed with wearability in mind. The wearability of haptic systems enables novel forms of communication, cooperation, and integration between humans and machines. Wearable haptic interfaces are capable of communicating with the human wearers during their interaction with the environment they share, in a natural and yet private way. This paper presents a taxonomy and review of wearable haptic systems for the fingertip and the hand, focusing on those systems directly addressing wearability challenges. The paper also discusses the main technological and design challenges for the development of wearable haptic interfaces, and it reports on the future perspectives of the field. Finally, the paper includes two tables summarizing the characteristics and features of the most representative wearable haptic systems for the fingertip and the hand

인간 기계 상호작용을 위한 강건하고 정확한 손동작 추적 기술 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 이동준.Hand-based interface is promising for realizing intuitive, natural and accurate human machine interaction (HMI), as the human hand is main source of dexterity in our daily activities. For this, the thesis begins with the human perception study on the detection threshold of visuo-proprioceptive conflict (i.e., allowable tracking error) with or without cutantoues haptic feedback, and suggests tracking error specification for realistic and fluidic hand-based HMI. The thesis then proceeds to propose a novel wearable hand tracking module, which, to be compatible with the cutaneous haptic devices spewing magnetic noise, opportunistically employ heterogeneous sensors (IMU/compass module and soft sensor) reflecting the anatomical properties of human hand, which is suitable for specific application (i.e., finger-based interaction with finger-tip haptic devices). This hand tracking module however loses its tracking when interacting with, or being nearby, electrical machines or ferromagnetic materials. For this, the thesis presents its main contribution, a novel visual-inertial skeleton tracking (VIST) framework, that can provide accurate and robust hand (and finger) motion tracking even for many challenging real-world scenarios and environments, for which the state-of-the-art technologies are known to fail due to their respective fundamental limitations (e.g., severe occlusions for tracking purely with vision sensors; electromagnetic interference for tracking purely with IMUs (inertial measurement units) and compasses; and mechanical contacts for tracking purely with soft sensors). The proposed VIST framework comprises a sensor glove with multiple IMUs and passive visual markers as well as a head-mounted stereo camera; and a tightly-coupled filtering-based visual-inertial fusion algorithm to estimate the hand/finger motion and auto-calibrate hand/glove-related kinematic parameters simultaneously while taking into account the hand anatomical constraints. The VIST framework exhibits good tracking accuracy and robustness, affordable material cost, light hardware and software weights, and ruggedness/durability even to permit washing. Quantitative and qualitative experiments are also performed to validate the advantages and properties of our VIST framework, thereby, clearly demonstrating its potential for real-world applications.손 동작을 기반으로 한 인터페이스는 인간-기계 상호작용 분야에서 직관성, 몰입감, 정교함을 제공해줄 수 있어 많은 주목을 받고 있고, 이를 위해 가장 필수적인 기술 중 하나가 손 동작의 강건하고 정확한 추적 기술 이다. 이를 위해 본 학위논문에서는 먼저 사람 인지의 관점에서 손 동작 추적 오차의 인지 범위를 규명한다. 이 오차 인지 범위는 새로운 손 동작 추적 기술 개발 시 중요한 설계 기준이 될 수 있어 이를 피험자 실험을 통해 정량적으로 밝히고, 특히 손끝 촉각 장비가 있을때 이 인지 범위의 변화도 밝힌다. 이를 토대로, 촉각 피드백을 주는 것이 다양한 인간-기계 상호작용 분야에서 널리 연구되어 왔으므로, 먼저 손끝 촉각 장비와 함께 사용할 수 있는 손 동작 추적 모듈을 개발한다. 이 손끝 촉각 장비는 자기장 외란을 일으켜 착용형 기술에서 흔히 사용되는 지자기 센서를 교란하는데, 이를 적절한 사람 손의 해부학적 특성과 관성 센서/지자기 센서/소프트 센서의 적절한 활용을 통해 해결한다. 이를 확장하여 본 논문에서는, 촉각 장비 착용 시 뿐 아니라 모든 장비 착용 / 환경 / 물체와의 상호작용 시에도 사용 가능한 새로운 손 동작 추적 기술을 제안한다. 기존의 손 동작 추적 기술들은 가림 현상 (영상 기반 기술), 지자기 외란 (관성/지자기 센서 기반 기술), 물체와의 접촉 (소프트 센서 기반 기술) 등으로 인해 제한된 환경에서 밖에 사용하지 못한다. 이를 위해 많은 문제를 일으키는 지자기 센서 없이 상보적인 특성을 지니는 관성 센서와 영상 센서를 융합하고, 이때 작은 공간에 다 자유도의 움직임을 갖는 손 동작을 추적하기 위해 다수의 구분되지 않는 마커들을 사용한다. 이 마커의 구분 과정 (correspondence search)를 위해 기존의 약결합 (loosely-coupled) 기반이 아닌 강결합 (tightly-coupled 기반 센서 융합 기술을 제안하고, 이를 통해 지자기 센서 없이 정확한 손 동작이 가능할 뿐 아니라 착용형 센서들의 정확성/편의성에 문제를 일으키던 센서 부착 오차 / 사용자의 손 모양 등을 자동으로 정확히 보정한다. 이 제안된 영상-관성 센서 융합 기술 (Visual-Inertial Skeleton Tracking (VIST)) 의 뛰어난 성능과 강건성이 다양한 정량/정성 실험을 통해 검증되었고, 이는 VIST의 다양한 일상환경에서 기존 시스템이 구현하지 못하던 손 동작 추적을 가능케 함으로써, 많은 인간-기계 상호작용 분야에서의 가능성을 보여준다.1 Introduction 1 1.1. Motivation 1 1.2. Related Work 5 1.3. Contribution 12 2 Detection Threshold of Hand Tracking Error 16 2.1. Motivation 16 2.2. Experimental Environment 20 2.2.1. Hardware Setup 21 2.2.2. Virtual Environment Rendering 23 2.2.3. HMD Calibration 23 2.3. Identifying the Detection Threshold of Tracking Error 26 2.3.1. Experimental Setup 27 2.3.2. Procedure 27 2.3.3. Experimental Result 31 2.4. Enlarging the Detection Threshold of Tracking Error by Haptic Feedback 31 2.4.1. Experimental Setup 31 2.4.2. Procedure 32 2.4.3. Experimental Result 34 2.5. Discussion 34 3 Wearable Finger Tracking Module for Haptic Interaction 38 3.1. Motivation 38 3.2. Development of Finger Tracking Module 42 3.2.1. Hardware Setup 42 3.2.2. Tracking algorithm 45 3.2.3. Calibration method 48 3.3. Evaluation for VR Haptic Interaction Task 50 3.3.1. Quantitative evaluation of FTM 50 3.3.2. Implementation of Wearable Cutaneous Haptic Interface 51 3.3.3. Usability evaluation for VR peg-in-hole task 53 3.4. Discussion 57 4 Visual-Inertial Skeleton Tracking for Human Hand 59 4.1. Motivation 59 4.2. Hardware Setup and Hand Models 62 4.2.1. Human Hand Model 62 4.2.2. Wearable Sensor Glove 62 4.2.3. Stereo Camera 66 4.3. Visual Information Extraction 66 4.3.1. Marker Detection in Raw Images 68 4.3.2. Cost Function for Point Matching 68 4.3.3. Left-Right Stereo Matching 69 4.4. IMU-Aided Correspondence Search 72 4.5. Filtering-based Visual-Inertial Sensor Fusion 76 4.5.1. EKF States for Hand Tracking and Auto-Calibration 78 4.5.2. Prediction with IMU Information 79 4.5.3. Correction with Visual Information 82 4.5.4. Correction with Anatomical Constraints 84 4.6. Quantitative Evaluation for Free Hand Motion 87 4.6.1. Experimental Setup 87 4.6.2. Procedure 88 4.6.3. Experimental Result 90 4.7. Quantitative and Comparative Evaluation for Challenging Hand Motion 95 4.7.1. Experimental Setup 95 4.7.2. Procedure 96 4.7.3. Experimental Result 98 4.7.4. Performance Comparison with Existing Methods for Challenging Hand Motion 101 4.8. Qualitative Evaluation for Real-World Scenarios 105 4.8.1. Visually Complex Background 105 4.8.2. Object Interaction 106 4.8.3. Wearing Fingertip Cutaneous Haptic Devices 109 4.8.4. Outdoor Environment 111 4.9. Discussion 112 5 Conclusion 116 References 124 Abstract (in Korean) 139 Acknowledgment 141박

Two Hand Gesture Based 3D Navigation in Virtual Environments

Natural interaction is gaining popularity due to its simple, attractive, and realistic nature, which realizes direct Human Computer Interaction (HCI). In this paper, we presented a novel two hand gesture based interaction technique for 3 dimensional (3D) navigation in Virtual Environments (VEs). The system used computer vision techniques for the detection of hand gestures (colored thumbs) from real scene and performed different navigation (forward, backward, up, down, left, and right) tasks in the VE. The proposed technique also allow users to efficiently control speed during navigation. The proposed technique is implemented via a VE for experimental purposes. Forty (40) participants performed the experimental study. Experiments revealed that the proposed technique is feasible, easy to learn and use, having less cognitive load on users. Finally gesture recognition engines were used to assess the accuracy and performance of the proposed gestures. kNN achieved high accuracy rates (95.7%) as compared to SVM (95.3%). kNN also has high performance rates in terms of training time (3.16 secs) and prediction speed (6600 obs/sec) as compared to SVM with 6.40 secs and 2900 obs/sec

Development of an active vision system for robot inspection of complex objects

Dissertação de mestrado integrado em Engenharia Mecânica (área de especialização em Sistemas Mecatrónicos)The dissertation presented here is in the scope of the IntVis4Insp project between University of Minho and the company Neadvance. It focuses on the development of a 3D hand tracking system that must be capable of extracting the hand position and orientation to prepare a manipulator for automatic inspection of leather pieces. This work starts with a literature review about the two main methods for collecting the necessary data to perform 3D hand tracking. These divide into glove-based methods and vision-based methods. The first ones work with some kind of support mounted on the hand that holds all the necessary sensors to measure the desired parameters. While the second ones recur to one or more cameras to capture the hands and through computer vision algorithms track their position and configuration. The selected method for this work was the vision-based method Openpose. For each recorded image, this application can locate 21 hand keypoints on each hand that together form a skeleton of the hands. This application is used in the tracking system developed throughout this dissertation. Its information is used in a more complete pipeline where the location of those hand keypoints is crucial to track the hands in videos of the demonstrated movements. These videos were recorded with an RGB-D camera, the Microsoft Kinect, which provides a depth value for every RGB pixel recorded. With the depth information and the 2D location of the hand keypoints in the images, it was possible to obtain the 3D world coordinates of these points considering the pinhole camera model. To define the hand, position a point is selected among the 21 for each hand, but for the hand orientation, it was necessary to develop an auxiliary method called “Iterative Pose Estimation Method” (ITP), which estimates the complete 3D pose of the hands. This method recurs only to the 2D locations of every hand keypoint, and the complete 3D world coordinates of the wrists to estimate the right 3D world coordinates of all the remaining points on the hand. This solution solves the problems related to hand occlusions that a prone to happen due to the use of only one camera to record the inspection videos. Once the world location of all the points in the hands is accurately estimated, their orientation can be defined by selecting three points forming a plane.A dissertação aqui apresentada insere-se no âmbito do projeto IntVis4Insp entre a Universidade do Minho e a empresa Neadavance, e foca-se no desenvolvimento de um sistema para extração da posição e orientação das mãos no espaço para posterior auxílio na manipulação automática de peças de couro, com recurso a manipuladores robóticos. O trabalho inicia-se com uma revisão literária sobre os dois principais métodos existentes para efetuar a recolha de dados necessária à monitorização da posição e orientação das mãos ao longo do tempo. Estes dividem-se em métodos baseados em luvas ou visão. No caso dos primeiros, estes recorrem normalmente a algum tipo de suporte montado na mão (ex.: luva em tecido), onde estão instalados todos os sensores necessários para a medição dos parâmetros desejados. Relativamente a sistemas de visão estes recorrem a uma câmara ou conjunto delas para capturar as mãos e por via de algoritmos de visão por computador determinam a sua posição e configuração. Foi selecionado para este trabalho um algoritmo de visão por computador denominado por Openpose. Este é capaz de, em cada imagem gravada e para cada mão, localizar 21 pontos pertencentes ao seu esqueleto. Esta aplicação é inserida no sistema de monitorização desenvolvido, sendo utilizada a sua informação numa arquitetura mais completa onde é efetuada a extração da localização dos pontos chave de cada mão nos vídeos de demonstração dos movimentos de inspeção. A gravação destes vídeos é efetuada com uma câmara RGB-D, a Microsoft Kinect, que fornece um valor de profundidade para cada pixel RGB gravado. Com os dados de profundidade e a localização dos pontos chave nas imagens foi possível obter as coordenadas 3D no mundo destes pontos considerando o modelo pinhole para a câmara. No caso da posição da mão é selecionado um ponto de entre os 21 para a definir ao longo do tempo, no entanto, para o cálculo da orientação foi desenvolvido um método auxiliar para estimação da pose tridimensional da mão denominado por “Iterative Pose Estimation Method” (ITP). Este método recorre aos dados 2D do Openpose e às coordenadas 3D do pulso de cada mão para efetuar a correta estimação das coordenadas 3D dos restantes pontos da mão. Isto permite essencialmente resolver problemas com oclusões da mão, muito frequentes com o uso de uma só câmara na gravação dos vídeos. Uma vez estimada corretamente a posição 3D no mundo dos vários pontos da mão, a sua orientação pode ser definida com recurso a quaisquer três pontos que definam um plano