Human Motion Reconstruction by Direct Control of Marker Trajectories (original) (raw)

Human Motion Reconstruction and Synthesis of Human Skills

Advances in Robot Kinematics: Motion in Man and Machine, 2010

Reconstructing human motion dynamics in real-time is a challenging problem since it requires accurate motion sensing, subject specific models, and efficient reconstruction algorithms. A promising approach is to construct accurate human models, and control them to behave the same way the subject does. Here, we demonstrate that the whole-body control approach can efficiently reconstruct a subject's motion dynamics in real world task-space when given a scaled model and marker based motion capture data. We scaled a biomechanically realistic musculoskeletal model to a subject, captured motion with suitably placed markers, and used an operational space controller to directly track the motion of the markers with the model. Our controller tracked the positions, velocities, and accelerations of many markers in parallel by assigning them to tasks with different priority levels based on how free their parent limbs were. We executed lower priority marker tracking tasks in the successive null spaces of the higher priority tasks to resolve their interdependencies. The controller accurately reproduced the subject's full body dynamics while executing a throwing motion in near real time. Its reconstruction closely matched the marker data, and its performance was consistent for the entire motion. Our findings suggest that the direct marker tracking approach is an attractive tool to reconstruct and synthesize the dynamic motion of humans and other complex articulated body systems in a computationally efficient manner.

Motion capture based human motion recognition and imitation by direct marker control

Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots, 2008

This paper deals with the imitation of human motions by a humanoid robot based on marker point measurements from a 3D motion capture system. For imitating the human's motion, we propose a Cartesian control approach in which a set of control points on the humanoid is selected and the robot is virtually connected to the measured marker points via translational springs. The forces according to these springs drive a simplified simulation of the robot dynamics, such that the real robot motion can finally be generated based on joint position controllers effectively managing joint friction and other uncertain dynamics. This procedure allows to make the robot follow the marker points without the need of explicitly computing inverse kinematics. For the implementation of the marker control on a humanoid robot, we combine it with a center of gravity based balancing controller for the lower body joints. We integrate the marker control based motion imitation with the mimesis model, which is a mathematical model for motion learning, recognition, and generation based on hidden Markov models (HMMs). Learning, recognition, and generation of motion primitives are all performed in marker coordinates paving the way for extending these concepts to task space problems and object manipulation. Finally, an experimental evaluation of the presented concepts using a 38 degrees of freedom humanoid robot is discussed.

Robotics-based Synthesis of Human Motion

The synthesis of human motion is a complex procedure that involves accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics and actuation, and characterization of reliable performance criteria. Many of these processes have much in common with the problems found in robotics research. Task-based methods used in robotics may be leveraged to provide novel musculoskeletal modeling methods and physiologically accurate performance predictions. In this paper, we present (i) a new method for the real-time reconstruction of human motion trajectories using direct marker tracking, (ii) a task-driven muscular effort minimization criterion and (iii) new human performance metrics for dynamic characterization of athletic skills. Dynamic motion reconstruction is achieved through the control of a simulated human model to follow the captured marker trajectories in real-time. The operational space control and real-time simulation provide human dynamics at any configuration of the performance. A new criteria of muscular effort minimization has been introduced to analyze human static postures. Extensive motion capture experiments were conducted to validate the new minimization criterion. Finally, new human performance metrics were introduced to study in details an athletic skill. These metrics include the effort expenditure and the feasible set of operational space accelerations during the performance of the skill. The dynamic characterization takes into account skeletal kinematics as well as muscle routing kinematics and force generating capacities. The developments draw upon an advanced musculoskeletal modeling platform and a task-oriented framework for the effective integration of biomechanics and robotics methods.

Task-Level Reconstruction and Analysis of Dynamic Motions in Human Musculoskeletal Systems

Understanding human motor control involves studying the principles used to optimize dynamic movement. This process requires accurate modeling and simulation of musculoskeletal kinematics, reconstruction of motion dynamics, and characterization of elite performance motor skills. Motivated by the previous robotics research applied to these challenges, the present research aims to leverage the task-level control strategies to reconstruct and analyze human dynamic motions, introducing the approaches of (i) modeling of human musculoskeletal system for task-level motion control, (ii) task-level reconstruction of human motion, (iii) robotics-based analysis of human dynamic skills. Our findings suggest that task-based control approach is an attractive tool to reconstruct and analyze the dynamic motions of human and other complex articulated body systems in a computationally efficient manner.

Reproduction of human arm movements using Kinect-based motion capture data

2013

Exploring the full potential of humanoid robots requires their ability to learn, generalize and reproduce complex tasks that will be faced in dynamic environments. In recent years, significant attention has been devoted to recovering kinematic information from the human motion using a motion capture system. This paper demonstrates and evaluates the use of a Kinect-based capture system that estimates the 3D human poses and converts them into gestures imitation in a robot. The main objectives are twofold: (1) to improve the initially estimated poses through a correction method based on constraint optimization, and (2) to present a method for computing the joint angles for the upper limbs corresponding to motion data from a human demonstrator. The feasibility of the approach is demonstrated by experimental results showing the upper-limb imitation of human actions by a robot model.

Challenges in Exploiting Prioritized Inverse Kinematics for Motion Capture and Postural Control

2005

In this paper we explore the potential of Prioritized Inverse Kinematics for motion capture and postural control. We have two goals in mind: reducing the number of sensors to improve the usability of such systems, and allowing interactions with the environment such as manipulating objects or managing collisions on the fly. To do so, we enforce some general constraints such as balance or others that we can infer from the intended movement structure. On one hand we may loose part of the expressiveness of the original movement but this is the price to pay to ensure more precise interactions with the environment.

Dynamic motion capture and edition using a stack of tasks

2011 11th IEEE-RAS International Conference on Humanoid Robots, 2011

This paper presents a complete methodology to quickly reshape a dynamic motion demonstrated by a human and to adapt the dynamics of the human to the dynamics of the robot. The method uses an inverse dynamics control scheme with a quadratic programming optimization solver. The motion data recorded using a motion capture system is introduced into the control scheme as a reference posture task to be followed by the joints trajectory respecting the dynamic limitations as well as the contact constraints. The motion is further modified using arbitrary tasks to let the robot imitate the original motion more closely or to make voluntary changes for aesthetic reasons. The results show the method applied to the humanoid robot HRP-2 imitating a human "pop dance".

Skeleton-based motion capture for robust reconstruction of human motion

Computer Animation …, 2000

Optical motion capture provides an impressive ability to replicate gestures. However, even with a highly professional system there are many instances where crucial markers are occluded or when the algorithm confuses the trajectory of one marker with that of another. This requires much editing work on the part of the animator before the virtual characters are ready for their screen debuts. In this paper, we present an approach to increasing the robustness of a motion capture system by using a sophisticated anatomic human model. It includes a precise description of the skeleton's mobility and an approximated envelope. It allows us to accurately predict the 3-D location and visibility of markers, thus significantly increasing the robustness of the marker tracking and assignment, and drastically reducing-or even eliminating-the need for human intervention during the 3-D reconstruction process.

Real-time human posture observation from a small number of joint measurements

Intelligent Robots and …, 2007

Measuring human movement in real time is of primary importance in a number of new applications of interactive systems and human centered robotics. A major difficulty in this field arises from the high joint redundancy of the human kinematics. Conventional approaches address this problem by installing a rather large number of sensors or markers on the subject. On the other hand, well admitted theories in neurosciences claim that joint synchronization in human movements is governed by so-called synergies, that can be viewed as joint patterns corresponding to a given gesture of a given individual. In this paper, we intend to exploit this property in order to reduce the number of sensors to be installed on a human subject when tracking his/her motion. Namely, our suggested method comprizes two phases. In a learning stage, the subject is asked to complete a given gesture a few times, while he/she is equipped with sensors able to measure his/her full posture. An algorithm is thus used in a second phase to reduce the required number of sensors while reconstructing the whole posture of the subject. Experimental evidence is provided for the particular motion of sit-to-stand transfer, in a study that involves healthy subjects.