On the Exact Recovery Conditions of 3D Human Motion from 2D Landmark Motion with Sparse Articulated Motion (original) (raw)

Optimal Reconstruction of Human Motion From Scarce Multimodal Data

IEEE Transactions on Human-Machine Systems, 2022

Wearable sensing has emerged as a promising solution for enabling unobtrusive and ergonomic measurements of the human motion. However, the reconstruction performance of these devices strongly depends on the quality and the number of sensors, which are typically limited by wearability and economic constraints. A promising approach to minimize the number of sensors is to exploit dimensionality reduction approaches that fuse prior information with insufficient sensing signals, through minimum variance estimation. These methods were successfully used for static hand pose reconstruction, but their translation to motion reconstruction has not been attempted yet. In this work, we propose the usage of functional principal component analysis to decompose multimodal, time-varying motion profiles in terms of linear combinations of basis functions. Functional decomposition enables the estimation of the a priori covariance matrix, and hence the fusion of scarce and noisy measured data with a priori information. We also consider the problem of identifying which elemental variables to measure as the most informative for a given class of tasks. We applied our method to two different datasets of upper limb motion D1 (joint trajectories) and D2 (joint trajectories + EMG data) considering an optimal set of measures (four joints for D1 out of seven, three joints, and eight EMGs for D2 out of seven and twelve, respectively). We found that our approach enables the reconstruction of upper limb motion with a median error of 0.013 ± 0.006 rad for D1 (relative median error 0.9%), and 0.038 ± 0.023 rad and 0.003 ± 0.002 mV for D2 (relative median error 2.9% and 5.1%, respectively).

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.

Observable subspaces for 3D human motion recovery

2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009

The articulated body models used to represent human motion typically have many degrees of freedom, usually expressed as joint angles that are highly correlated. The true range of motion can therefore be represented by latent variables that span a low-dimensional space.

Motion-Based View-Invariant Articulated Motion Detection and Pose Estimation Using Sparse Point Features

Lecture Notes in Computer Science, 2009

We present an approach for articulated motion detection and pose estimation that uses only motion information. To estimate the pose and viewpoint we introduce a novel motion descriptor that computes the spatial relationships of motion vectors representing various parts of the person using the trajectories of a number of sparse points. A nearest neighbor search for the closest motion descriptor from the labeled training data of human walking poses in multiple views is performed. This observational probability is fed to a Hidden Markov Model defined over multiple poses and viewpoints to obtain temporally consistent pose estimates. Experimental results on various sequences of walking subjects with multiple viewpoints demonstrate the effectiveness of the approach. In particular, our purely motion-based approach is able to track people even when other visible cues are not available, such as in low-light situations.

A New Robust Motion Reconstruction Method based on Optimization with Redundant Constraints and Natural Coordinates

2011

The three-dimensional analysis of human movement is of interest in many different fields of life sciences, computer animation and engineering. The elements involved in the analysis of human movement are usually measurement equipments for estimating kinematic, kinetic and myoelectric variables, mathematical models of the human musculoskeletal system, and mathematical methods for calculating the variables which cannot be directly measured. The aim of this thesis is to advance in the knowledge of four aspects of the three-dimensional analysis of the human movement: 1) the motion reconstruction of human movements using large and medium-size skeletal models with openand closed-loops, 2) two problems inherent to optoelectronic motion capture systems: the missing marker problem and the impossibility of measuring completely the motion of some bones which move under the skin, 3) the estimation of subject-specific parameters using only a motion capture system, and 4) the development of severa...

Human motion capture driven by orientation measurements

Teleoperators and Virtual Environments, 1998

Motion-capture techniques are rarely based on orientation measurements for two main reasons: (1) optical motion-capture systems are designed for tracking object position rather than their orientation (which can be deduced from several trackers), (2) known animation techniques, like inverse kinematics or geometric algorithms, require position targets constantly, but orientation inputs only occasionally. We propose a complete human motion-capture technique based essentially on orientation measurements. The position measurement is used only for recovering the global position of the performer. This method allows fast tracking of human gestures for interactive applications as well as high rate recording. Several motion-capture optimizations, including the multijoint technique, improve the posture realism. This work is well suited for magnetic-based systems that rely more on orientation registration (in our environment) than position measurements that necessitate difficult system calibration.

Spatial reconstruction of the human motion based on images of a single camera

Journal of Biomechanics, 2001

The inverse dynamic analysis procedures used in the study of the human gait require that the kinematics of the supporting biomechanical model is known beforehand. The first step to obtain the kinematic data is the reconstruction of human spatial motion, i.e., the evaluation of the anatomic points positions that enables to uniquely define the position of all anatomical segments. In photogrammetry, the projection of each anatomical point is described by two linear equations relating its three spatial coordinates with the two coordinates of the projected point. The need for the image of two cameras arises from the fact that three equations are necessary to find the original spatial position of the anatomical point. It is shown here that the kinematic constraint equations associated with a biomechanical model can be used as the extra set of equations required for the reconstruction process, instead of the equations associated with the second camera. With this methodology, the system of equations arising from the point projections and biomechanical model kinematic constraints are solved simultaneously. Since the system of equations has multiple solutions for each image, a strategy based on the minimization of the cost function associated to the smoothness of the reconstructed motion is devised, leading to an automated computer procedure enabling a unique reconstruction. r

Human Motion Reconstruction by Direct Control of Marker Trajectories

Advances in Robot Kinematics: Analysis and Design, 2008

Understanding the basis of human movement and reproducing it in robotic environments is a compelling challenge that has engaged a multidisciplinary audience. In addressing this challenge, an important initial step involves reconstructing motion from experimental motion capture data. To this end we propose a new algorithm to reconstruct human motion from motion capture data through direct control of captured marker trajectories. This algorithm is based on a task/posture decomposition and prioritized control approach. This approach ensures smooth tracking of desired marker trajectories as well as the extraction of joint angles in real-time without the need for inverse kinematics. It also provides flexibility over traditional inverse kinematic approaches. Our algorithm was validated on a sequence of tai chi motions. The results demonstrate the efficacy of the direct marker control approach for motion reconstruction from experimental marker data.