Accurate Human Motion Capture and Modeling using Low-cost Sensors

Motion capture technologies, especially those combined with multiple kinds of sensory technologies to capture both kinematic and dynamic information, are widely used in a variety of fields such as biomechanics, robotics, and health. However, many existing systems suffer from limitations of being intrusive, restrictive, and expensive. This dissertation explores two aspects of motion capture systems that are low-cost, non-intrusive, high-accuracy, and easy to use for common users, including both full-body kinematics and dynamics capture, and user-specific hand modeling. More specifically, we present a new method for full-body motion capture that uses input data captured by three depth cameras and a pair of pressure-sensing shoes. Our system is appealing because it is fully automatic and can accurately reconstruct both full-body kinematic and dynamic data. We introduce a highly accurate tracking process that automatically reconstructs 3D skeletal poses using depth data, foot pressure data, and detailed full-body geometry. We also develop an efficient physics-based motion reconstruction algorithm for solving internal joint torques and contact forces based on contact pressure information and 3D poses from the kinematic tracking process. In addition, we present a novel low-dimensional parametric model for 3D hand modeling and synthesis. We construct a low-dimensional parametric model to compactly represent hand shape variations across individuals and enhance it by adding Linear Blend Skinning (LBS) for pose deformation. We also introduce an efficient iterative approach to learn the parametric model from a large unaligned scan database. Our model is compact, expressive, and produces a natural-looking LBS model for pose deformation, which allows for a variety of applications ranging from user-specific hand modeling to skinning weights transfer and model-based hand tracking

A survey on human performance capture and animation

With the rapid development of computing technology, three-dimensional (3D) human body models and their dynamic motions are widely used in the digital entertainment industry. Human perfor- mance mainly involves human body shapes and motions. Key research problems include how to capture and analyze static geometric appearance and dynamic movement of human bodies, and how to simulate human body motions with physical e�ects. In this survey, according to main research directions of human body performance capture and animation, we summarize recent advances in key research topics, namely human body surface reconstruction, motion capture and synthesis, as well as physics-based motion sim- ulation, and further discuss future research problems and directions. We hope this will be helpful for readers to have a comprehensive understanding of human performance capture and animatio

Data-Driven Approach to Simulating Realistic Human Joint Constraints

Modeling realistic human joint limits is important for applications involving physical human-robot interaction. However, setting appropriate human joint limits is challenging because it is pose-dependent: the range of joint motion varies depending on the positions of other bones. The paper introduces a new technique to accurately simulate human joint limits in physics simulation. We propose to learn an implicit equation to represent the boundary of valid human joint configurations from real human data. The function in the implicit equation is represented by a fully connected neural network whose gradients can be efficiently computed via back-propagation. Using gradients, we can efficiently enforce realistic human joint limits through constraint forces in a physics engine or as constraints in an optimization problem.Comment: To appear at ICRA 2018; 6 pages, 9 figures; for associated video, see https://youtu.be/wzkoE7wCbu

Comprehensive and accurate estimation of lower body movement using few wearable sensors

Human pose estimation involves tracking the position and orientation (i.e., pose) of body segments, and estimating joint kinematics. It finds application in robotics, virtual reality, animation, and healthcare. Recent miniaturisation of inertial measurement units (IMUs) has paved the path towards inertial motion capture systems (MCS) suitable for use in unstructured environments. However, commercial inertial MCS attach one-sensor-per-body-segment (OSPS) which can be too cumbersome for daily use. A reduced-sensor-count configuration, where IMUs are placed on a subset of body segments, can improve user comfort while also reducing setup time and system cost. This work aims to develop pose estimation algorithms that track lower body motion using as few sensors as possible, towards developing a comfortable MCS for daily routine use. Such a tool can facilitate interactive rehabilitation, performance improvement, and the study of movement disorder progression to potentially enable predictive diagnostics. This thesis presents pose estimation algorithms that utilise biomechanical constraints, additional distance measurements, and balance assumptions to infer information lost from using less sensors. Specifically, it presents a novel use of Lie group representation of pose alongside a constrained extended Kalman filter for estimating pelvis, thigh, shank, and foot kinematics using only two or three IMUs. The algorithms iteratively use linear kinematic equations to predict the next state, leverage indirect observations and assumptions (e.g., pelvis height, zero-velocity update, flat-floor assumption, inter-IMU distance), and enforce biomechanical constraints (e.g., constant body segment length, hinged knee joints, range of motion). The algorithm was comprehensively evaluated on nine healthy subjects who performed free walking, jogging, and other random movements within a 4x4 m2 room using benchmark optical and inertial (i.e., OSPS) MCS. In contrast to existing benchmark datasets, both direct kinematics (e.g., Vicon plug-in gait commonly used in gait analysis) and inverse kinematics (used in robotics and musculoskeletal modelling) pose reconstruction, along with the corresponding measurements, are shared publicly. The mean position root-mean-square error relative to the mid-pelvis origin was 5.3±1.0 cm, while the sagittal knee and hip joint angle correlation coefficients were 0.85±0.05 and 0.89±0.05 indicating promising performance for joint kinematics in the sagittal plane

The Supernumerary Robotic 3rd Thumb for Skilled Music Tasks

Wearable robotics bring the opportunity to augment human capability and performance, be it through prosthetics, exoskeletons, or supernumerary robotic limbs. The latter concept allows enhancing human performance and assisting them in daily tasks. An important research question is, however, whether the use of such devices can lead to their eventual cognitive embodiment, allowing the user to adapt to them and use them seamlessly as any other limb of their own. This paper describes the creation of a platform to investigate this. Our supernumerary robotic 3rd thumb was created to augment piano playing, allowing a pianist to press piano keys beyond their natural hand-span; thus leading to functional augmentation of their skills and the technical feasibility to play with 11 fingers. The robotic finger employs sensors, motors, and a human interfacing algorithm to control its movement in real-time. A proof of concept validation experiment has been conducted to show the effectiveness of the robotic finger in playing musical pieces on a grand piano, showing that naive users were able to use it for 11 finger play within a few hours

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

동영상 속 사람 동작의 물리 기반 재구성 및 분석

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2021. 2. 이제희.In computer graphics, simulating and analyzing human movement have been interesting research topics started since the 1960s. Still, simulating realistic human movements in a 3D virtual world is a challenging task in computer graphics. In general, motion capture techniques have been used. Although the motion capture data guarantees realistic result and high-quality data, there is lots of equipment required to capture motion, and the process is complicated. Recently, 3D human pose estimation techniques from the 2D video are remarkably developed. Researchers in computer graphics and computer vision have attempted to reconstruct the various human motions from video data. However, existing methods can not robustly estimate dynamic actions and not work on videos filmed with a moving camera. In this thesis, we propose methods to reconstruct dynamic human motions from in-the-wild videos and to control the motions. First, we developed a framework to reconstruct motion from videos using prior physics knowledge. For dynamic motions such as backspin, the poses estimated by a state-of-the-art method are incomplete and include unreliable root trajectory or lack intermediate poses. We designed a reward function using poses and hints extracted from videos in the deep reinforcement learning controller and learned a policy to simultaneously reconstruct motion and control a virtual character. Second, we simulated figure skating movements in video. Skating sequences consist of fast and dynamic movements on ice, hindering the acquisition of motion data. Thus, we extracted 3D key poses from a video to then successfully replicate several figure skating movements using trajectory optimization and a deep reinforcement learning controller. Third, we devised an algorithm for gait analysis through video of patients with movement disorders. After acquiring the patients joint positions from 2D video processed by a deep learning network, the 3D absolute coordinates were estimated, and gait parameters such as gait velocity, cadence, and step length were calculated. Additionally, we analyzed the optimization criteria of human walking by using a 3D musculoskeletal humanoid model and physics-based simulation. For two criteria, namely, the minimization of muscle activation and joint torque, we compared simulation data with real human data for analysis. To demonstrate the effectiveness of the first two research topics, we verified the reconstruction of dynamic human motions from 2D videos using physics-based simulations. For the last two research topics, we evaluated our results with real human data.컴퓨터 그래픽스에서 인간의 움직임 시뮬레이션 및 분석은 1960 년대부터 다루어진 흥미로운 연구 주제이다. 몇 십년 동안 활발하게 연구되어 왔음에도 불구하고, 3차원 가상 공간 상에서 사실적인 인간의 움직임을 시뮬레이션하는 연구는 여전히 어렵고 도전적인 주제이다. 그동안 사람의 움직임 데이터를 얻기 위해서 모션 캡쳐 기술이 사용되어 왔다. 모션 캡처 데이터는 사실적인 결과와 고품질 데이터를 보장하지만 모션 캡쳐를 하기 위해서 필요한 장비들이 많고, 그 과정이 복잡하다. 최근에 2차원 영상으로부터 사람의 3차원 자세를 추정하는 연구들이 괄목할 만한 결과를 보여주고 있다. 이를 바탕으로 컴퓨터 그래픽스와 컴퓨터 비젼 분야의 연구자들은 비디오 데이터로부터 다양한 인간 동작을 재구성하려는 시도를 하고 있다. 그러나 기존의 방법들은 빠르고 다이나믹한 동작들은 안정적으로 추정하지 못하며 움직이는 카메라로 촬영한 비디오에 대해서는 작동하지 않는다. 본 논문에서는 비디오로부터 역동적인 인간 동작을 재구성하고 동작을 제어하는 방법을 제안한다. 먼저 사전 물리학 지식을 사용하여 비디오에서 모션을 재구성하는 프레임 워크를 제안한다. 공중제비와 같은 역동적인 동작들에 대해서 최신 연구 방법을 동원하여 추정된 자세들은 캐릭터의 궤적을 신뢰할 수 없거나 중간에 자세 추정에 실패하는 등 불완전하다. 우리는 심층강화학습 제어기에서 영상으로부터 추출한 포즈와 힌트를 활용하여 보상 함수를 설계하고 모션 재구성과 캐릭터 제어를 동시에 하는 정책을 학습하였다. 둘 째, 비디오에서 피겨 스케이팅 기술을 시뮬레이션한다. 피겨 스케이팅 기술들은 빙상에서 빠르고 역동적인 움직임으로 구성되어 있어 모션 데이터를 얻기가 까다롭다. 비디오에서 3차원 키 포즈를 추출하고 궤적 최적화 및 심층강화학습 제어기를 사용하여 여러 피겨 스케이팅 기술을 성공적으로 시연한다. 셋 째, 파킨슨 병이나 뇌성마비와 같은 질병으로 인하여 움직임 장애가 있는 환자의 보행을 분석하기 위한 알고리즘을 제안한다. 2차원 비디오로부터 딥러닝을 사용한 자세 추정기법을 사용하여 환자의 관절 위치를 얻어낸 다음, 3차원 절대 좌표를 얻어내어 이로부터 보폭, 보행 속도와 같은 보행 파라미터를 계산한다. 마지막으로, 근골격 인체 모델과 물리 시뮬레이션을 이용하여 인간 보행의 최적화 기준에 대해 탐구한다. 근육 활성도 최소화와 관절 돌림힘 최소화, 두 가지 기준에 대해 시뮬레이션한 후, 실제 사람 데이터와 비교하여 결과를 분석한다. 처음 두 개의 연구 주제의 효과를 입증하기 위해, 물리 시뮬레이션을 사용하여 이차원 비디오로부터 재구성한 여러 가지 역동적인 사람의 동작들을 재현한다. 나중 두 개의 연구 주제는 사람 데이터와의 비교 분석을 통하여 평가한다.1 Introduction 1 2 Background 9 2.1 Pose Estimation from 2D Video . . . . . . . . . . . . . . . . . . . . 9 2.2 Motion Reconstruction from Monocular Video . . . . . . . . . . . . 10 2.3 Physics-Based Character Simulation and Control . . . . . . . . . . . 12 2.4 Motion Reconstruction Leveraging Physics . . . . . . . . . . . . . . 13 2.5 Human Motion Control . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.5.1 Figure Skating Simulation . . . . . . . . . . . . . . . . . . . 16 2.6 Objective Gait Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.7 Optimization for Human Movement Simulation . . . . . . . . . . . . 17 2.7.1 Stability Criteria . . . . . . . . . . . . . . . . . . . . . . . . 18 3 Human Dynamics from Monocular Video with Dynamic Camera Movements 19 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.3 Pose and Contact Estimation . . . . . . . . . . . . . . . . . . . . . . 21 3.4 Learning Human Dynamics . . . . . . . . . . . . . . . . . . . . . . . 24 3.4.1 Policy Learning . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.4.2 Network Training . . . . . . . . . . . . . . . . . . . . . . . . 28 3.4.3 Scene Estimator . . . . . . . . . . . . . . . . . . . . . . . . 29 3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5.1 Video Clips . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5.2 Comparison of Contact Estimators . . . . . . . . . . . . . . . 33 3.5.3 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.5.4 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4 Figure Skating Simulation from Video 42 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.2 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.3 Skating Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.3.1 Non-holonomic Constraints . . . . . . . . . . . . . . . . . . 46 4.3.2 Relaxation of Non-holonomic Constraints . . . . . . . . . . . 47 4.4 Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.5 Trajectory Optimization and Control . . . . . . . . . . . . . . . . . . 50 4.5.1 Trajectory Optimization . . . . . . . . . . . . . . . . . . . . 50 4.5.2 Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5 Gait Analysis Using Pose Estimation Algorithm with 2D-video of Patients 61 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.2.1 Patients and video recording . . . . . . . . . . . . . . . . . . 63 5.2.2 Standard protocol approvals, registrations, and patient consents 66 5.2.3 3D Pose estimation from 2D video . . . . . . . . . . . . . . . 66 5.2.4 Gait parameter estimation . . . . . . . . . . . . . . . . . . . 67 5.2.5 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . 68 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5.3.1 Validation of video-based analysis of the gait . . . . . . . . . 68 5.3.2 gait analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.4.1 Validation with the conventional sensor-based method . . . . 75 5.4.2 Analysis of gait and turning in TUG . . . . . . . . . . . . . . 75 5.4.3 Correlation with clinical parameters . . . . . . . . . . . . . . 76 5.4.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5.5 Supplementary Material . . . . . . . . . . . . . . . . . . . . . . . . . 77 6 Control Optimization of Human Walking 80 6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 6.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6.2.1 Musculoskeletal model . . . . . . . . . . . . . . . . . . . . . 82 6.2.2 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6.2.3 Control co-activation level . . . . . . . . . . . . . . . . . . . 83 6.2.4 Push-recovery experiment . . . . . . . . . . . . . . . . . . . 84 6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 7 Conclusion 90 7.1 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Docto

Non-contact measures to monitor hand movement of people with rheumatoid arthritis using a monocular RGB camera

Hand movements play an essential role in a person’s ability to interact with the environment. In hand biomechanics, the range of joint motion is a crucial metric to quantify changes due to degenerative pathologies, such as rheumatoid arthritis (RA). RA is a chronic condition where the immune system mistakenly attacks the joints, particularly those in the hands. Optoelectronic motion capture systems are gold-standard tools to quantify changes but are challenging to adopt outside laboratory settings. Deep learning executed on standard video data can capture RA participants in their natural environments, potentially supporting objectivity in remote consultation. The three main research aims in this thesis were 1) to assess the extent to which current deep learning architectures, which have been validated for quantifying motion of other body segments, can be applied to hand kinematics using monocular RGB cameras, 2) to localise where in videos the hand motions of interest are to be found, 3) to assess the validity of 1) and 2) to determine disease status in RA. First, hand kinematics for twelve healthy participants, captured with OpenPose were benchmarked against those captured using an optoelectronic system, showing acceptable instrument errors below 10°. Then, a gesture classifier was tested to segment video recordings of twenty-two healthy participants, achieving an accuracy of 93.5%. Finally, OpenPose and the classifier were applied to videos of RA participants performing hand exercises to determine disease status. The inferred disease activity exhibited agreement with the in-person ground truth in nine out of ten instances, outperforming virtual consultations, which agreed only six times out of ten. These results demonstrate that this approach is more effective than estimated disease activity performed by human experts during video consultations. The end goal sets the foundation for a tool that RA participants can use to observe their disease activity from their home.Open Acces

Egocentric Reconstruction of Human Bodies for Real-time Mobile Telepresence

A mobile 3D acquisition system has the potential to make telepresence significantly more convenient, available to users anywhere, anytime, without relying on any instrumented environments. Such a system can be implemented using egocentric reconstruction methods, which rely only on wearable sensors, such as head-worn cameras and body-worn inertial measurement units. Prior egocentric reconstruction methods suffer from incomplete body visibility as well as insufficient sensor data. This dissertation investigates an egocentric 3D capture system relying only on sensors embedded in commonly worn items such as eyeglasses, wristwatches, and shoes. It introduces three advances in egocentric reconstruction of human bodies. (1) A parametric-model-based reconstruction method that overcomes incomplete body surface visibility by estimating the user's body pose and facial expression, and using the results to re-target a high-fidelity pre-scanned model of the user. (2) A learning-based visual-inertial body motion reconstruction system that relies only on eyeglasses-mounted cameras and a few body-worn inertial sensors. This approach overcomes the challenges of self-occlusion and outside-of-camera motions, and allows for unobtrusive real-time 3D capture of the user. (3) A physically plausible reconstruction method based on rigid body dynamics, which reduces motion jitter and prevents interpenetrations between the reconstructed user's model and the objects in the environment such as the ground, walls, and furniture. This dissertation includes experimental results demonstrating the real-time, mobile reconstruction of human bodies in indoor and outdoor scenes, relying only on wearable sensors embedded in commonly-worn objects and overcoming the sparse observation challenges of egocentric reconstruction. The potential usefulness of this approach is demonstrated in a telepresence scenario featuring physical therapy training.Doctor of Philosoph