Franck Multon

Being marker-free and calibration free, Microsoft Kinect is nowadays widely used in many motion-based applications, such as user training for complex industrial tasks and ergonomics pose evaluation. The major problem of Kinect is the placement requirement to obtain accurate poses, as well as its weakness against occlusions. To improve the robustness of Kinect in interactive motion-based applications, real-time data-driven pose reconstruction has been proposed. The idea is to utilize a database… Voir plus

Being marker-free and calibration free, Microsoft Kinect is nowadays widely used in many motion-based applications, such as user training for complex industrial tasks and ergonomics pose evaluation. The major problem of Kinect is the placement requirement to obtain accurate poses, as well as its weakness against occlusions. To improve the robustness of Kinect in interactive motion-based applications, real-time data-driven pose reconstruction has been proposed. The idea is to utilize a database of accurately captured human poses as a prior to optimize the Kinect recognized ones, in order to estimate the true poses performed by the user. The key research problem is to identify the most relevant poses in the database for accurate and efficient reconstruction. In this paper, we propose a new pose reconstruction method based on modelling the pose database with a structure called Filtered Pose Graph, which indicates the intrinsic correspondence between poses. Such a graph not only speeds up the database poses selection process, but also improves the relevance of the selected poses for higher quality reconstruction. We apply the proposed method in a challenging environment of industrial context that involves sub-optimal Kinect placement and a large amount of occlusion. Experimental results show that our real-time system reconstructs Kinect poses more accurately than existing methods. Voir moins

Accepted for publication as Regular Paper.

Accurate distance perception and natural interactions are mandatory conditions when training precision aiming tasks in VR. However, many factors specific to virtual environments (VEs) lead to differences in the way users execute a motor task in VR versus the real world. To investigate these differences, the authors performed a study on basketball beginners' free-throw performance in VEs under different visual conditions. Although the success rate is not statistically different, some adaptations… Voir plus

Accurate distance perception and natural interactions are mandatory conditions when training precision aiming tasks in VR. However, many factors specific to virtual environments (VEs) lead to differences in the way users execute a motor task in VR versus the real world. To investigate these differences, the authors performed a study on basketball beginners' free-throw performance in VEs under different visual conditions. Although the success rate is not statistically different, some adaptations occurred in the way the users performed the task, depending on the visual conditions. In the third-person perspective visual condition, the release parameters indicate that the users more accurately estimated distance to target. Adding visual guidance information (gradual depth information showing the ideal ball trajectory) also led to more natural motor behavior. The final aim of this study was to develop a reliable basketball free-throw training system in VEs, so the authors compared beginners' performances in VR with experts' models of performance. Their results show that most of the performance variables tended to evolve closer to the experts' performance during the training in the VE. Voir moins

Background: Various asymmetry indices have been proposed to compare the spatiotemporal, kinematic and kinetic parameters of lower limbs during the gait cycle. However, these indices rely on gait measurement systems that are costly and generally require manual examination, calibration procedures and the precise placement of sensors/markers on the body of the patient. Methods: To overcome these issues, this paper proposes a new asymmetry index, which uses an inexpensive, easy-to-use and… Voir plus

Background: Various asymmetry indices have been proposed to compare the spatiotemporal, kinematic and kinetic parameters of lower limbs during the gait cycle. However, these indices rely on gait measurement systems that are costly and generally require manual examination, calibration procedures and the precise placement of sensors/markers on the body of the patient. Methods: To overcome these issues, this paper proposes a new asymmetry index, which uses an inexpensive, easy-to-use and markerless depth camera (Microsoft Kinect™) output. This asymmetry index directly uses depth images provided by the Kinect™ without requiring joint localization. It is based on the longitudinal spatial difference between lower-limb movements during the gait cycle. To evaluate the relevance of this index, fifteen healthy subjects were tested on a treadmill walking normally and then via an artificially-induced gait asymmetry with a thick sole placed under one shoe. The gait movement was simultaneously recorded using a Kinect™ placed in front of the subject and a motion capture system. Results: The proposed longitudinal index distinguished asymmetrical gait (p < 0.001), while other symmetry indices based on spatiotemporal gait parameters failed using such Kinect™ skeleton measurements. Moreover, the correlation coefficient between this index measured by Kinect™ and the ground truth of this index measured by motion capture is 0.968. Conclusion: This gait asymmetry index measured with a Kinect™ is low cost, easy to use and is a promising development for clinical gait analysis. Voir moins

Treadmill walking is commonly used to analyze several gait cycles in a limited space. Depth cameras, such as the low-cost and easy-to-use Kinect sensor, look promising for gait analysis on a treadmill for routine outpatient clinics. However, gait analysis is based on accurately detecting gait events (such as heel-strike) by tracking the feet which may be incorrectly recognized with Kinect. Indeed depth images could lead to confusion between the ground and the feet around the contact phase. To… Voir plus

Treadmill walking is commonly used to analyze several gait cycles in a limited space. Depth cameras, such as the low-cost and easy-to-use Kinect sensor, look promising for gait analysis on a treadmill for routine outpatient clinics. However, gait analysis is based on accurately detecting gait events (such as heel-strike) by tracking the feet which may be incorrectly recognized with Kinect. Indeed depth images could lead to confusion between the ground and the feet around the contact phase. To tackle this problem we assume that heel-strike events could be indirectly estimated by searching for extreme values of the distance between knee joints along the walking longitudinal axis. To evaluate this assumption, the motion of 11 healthy subjects walking on a treadmill was recorded using both an optoelectronic system and Kinect. The measures were compared to reference heel-strike events obtained with vertical foot velocity. When using the optoelectronic system to assess knee joints, heel-strike estimation errors were very small (29 ± 18 ms) leading to small cycle durations errors (0 ± 15 ms). To locate knees in depth map (Kinect), we used anthropometrical data to select the body point located at a constant height where the knee should be based on a reference posture. This Kinect approach gave heel-strike errors of 17 ± 24 ms (mean cycle duration error: 0 ± 12 ms). Using this same anthropometric methodology with optoelectronic data, the heel-strike error was 12 ± 12 ms (mean cycle duration error: 0 ± 11 ms). Compared to previous studies using Kinect, heel-strike and gait cycles were more accurately estimated, which could improve clinical gait analysis with such sensor. Voir moins

Background: Various asymmetry indices have been proposed to compare the spatiotemporal, kinematic and kinetic parameters of lower limbs during the gait cycle. However, these indices rely on gait measurement systems that are costly and generally require manual examination, calibration procedures and the precise placement of sensors/markers on the body of the patient. Methods: To overcome these issues, this paper proposes a new asymmetry index, which uses an inexpensive, easy-to-use and… Voir plus

Background: Various asymmetry indices have been proposed to compare the spatiotemporal, kinematic and kinetic parameters of lower limbs during the gait cycle. However, these indices rely on gait measurement systems that are costly and generally require manual examination, calibration procedures and the precise placement of sensors/markers on the body of the patient. Methods: To overcome these issues, this paper proposes a new asymmetry index, which uses an inexpensive, easy-to-use and markerless depth camera (Microsoft Kinect™) output. This asymmetry index directly uses depth images provided by the Kinect™ without requiring joint localization. It is based on the longitudinal spatial difference between lower-limb movements during the gait cycle. To evaluate the relevance of this index, fifteen healthy subjects were tested on a treadmill walking normally and then via an artificially-induced gait asymmetry with a thick sole placed under one shoe. The gait movement was simultaneously recorded using a Kinect™ placed in front of the subject and a motion capture system. Results: The proposed longitudinal index distinguished asymmetrical gait (p < 0.001), while other symmetry indices based on spatiotemporal gait parameters failed using such Kinect™ skeleton measurements. Moreover, the correlation coefficient between this index measured by Kinect™ and the ground truth of this index measured by motion capture is 0.968. Conclusion: This gait asymmetry index measured with a Kinect™ is low cost, easy to use and is a promising development for clinical gait analysis. Voir moins

Analyzing human poses with a Kinect is a promising method to evaluate potentials risks of musculoskeletal disorders at workstations. In ecological situations, complex 3D poses and constraints imposed by the environment make it difficult to obtain reliable kinematic information. Thus, being able to predict the potential accuracy of the measurement for such complex 3D poses and sensor placements is challenging in classical experimental setups. To tackle this problem, we propose a new evaluation… Voir plus

Analyzing human poses with a Kinect is a promising method to evaluate potentials risks of musculoskeletal disorders at workstations. In ecological situations, complex 3D poses and constraints imposed by the environment make it difficult to obtain reliable kinematic information. Thus, being able to predict the potential accuracy of the measurement for such complex 3D poses and sensor placements is challenging in classical experimental setups. To tackle this problem, we propose a new evaluation method based on a virtual mannequin. In this study, we apply this method to the evaluation of joint positions (shoulder, elbow, and wrist), joint angles (shoulder and elbow), and the corresponding RULA (a popular ergonomics assessment grid) upper-limb score for a large set of poses and sensor placements. Thanks to this evaluation method, more than 500,000 configurations have been automatically tested, which would be almost impossible to evaluate with classical protocols. The results show that the kinematic information obtained by the Kinect software is generally accurate enough to fill in ergonomic assessment grids. However inaccuracy strongly increases for some specific poses and sensor positions. Using this evaluation method enabled us to report configurations that could lead to these high inaccuracies. As a supplementary material, we provide a software tool to help designers to evaluate the expected accuracy of this sensor for a set of upper-limb configurations. Results obtained with the virtual mannequin are in accordance with those obtained from a real subject for a limited set of poses and sensor placements. Voir moins

Highlights:
+ We automatically compute limb contact configurations for animating motion tasks.
+ The quality of the configurations is ensured by a heuristic for task efficiency.
+ We obtain real time performances for arbitrary creatures and environments.
+ A precise definition of the human range of motion enhances more natural results.

Abstract:
A common issue in three-dimensional animation is the creation of contacts between a virtual creature and the… Voir plus

Highlights:
+ We automatically compute limb contact configurations for animating motion tasks.
+ The quality of the configurations is ensured by a heuristic for task efficiency.
+ We obtain real time performances for arbitrary creatures and environments.
+ A precise definition of the human range of motion enhances more natural results.

Abstract:
A common issue in three-dimensional animation is the creation of contacts between a virtual creature and the environment. Contacts allow force exertion, which produces motion. This paper addresses the problem of computing contact configurations allowing to perform motion tasks such as getting up from a sofa, pushing an object or climbing. We propose a two-step method to generate contact configurations suitable for such tasks. The first step is an offline sampling of the range of motion (ROM) of a virtual creature. The ROM of the human arms and legs is precisely determined experimentally. The second step is a run time request confronting the samples with the current environment. The best contact configurations are then selected according to a heuristic for task efficiency. The heuristic is inspired by the force transmission ratio. Given a contact configuration, it measures the potential force that can be exerted in a given direction. The contact configurations are then used as inputs for an inverse kinematics solver that will compute the final animation. Our method is automatic and does not require examples or motion capture data. It is suitable for real time applications and applies to arbitrary creatures in arbitrary environments. Various scenarios (such as climbing, crawling, getting up, pushing or pulling objects) are used to demonstrate that our method enhances motion autonomy and interactivity in constrained environments. Voir moins

In this paper, we propose a novel idea to ad-dress the problem of fast computation of enveloping grasp configurations for a multi-fingered hand with 3D polygonal models that are polygon soups. The proposed method performs a low-level shape matching by wrapping multiple cords around an object in order to quickly isolate promising grasping spots. From these spots, hand palm posture can be computed followed by a standard close-until-contact procedure to find the contact points. Along with the… Voir plus

In this paper, we propose a novel idea to ad-dress the problem of fast computation of enveloping grasp configurations for a multi-fingered hand with 3D polygonal models that are polygon soups. The proposed method performs a low-level shape matching by wrapping multiple cords around an object in order to quickly isolate promising grasping spots. From these spots, hand palm posture can be computed followed by a standard close-until-contact procedure to find the contact points. Along with the contacts information, the finger kinematics is then used to filter the unstable grasps. Through multiple simulated examples with a twelve degrees-of-freedom anthropomorphic hand, we demonstrate that our method can compute good grasps for objects with complex geometries in a short amount of time. Best of all, this is achieved without complex model preprocessing like segmentation by parts and medial axis extraction. Voir moins

Multiped locomotion in cluttered environments is addressed as the prob-
lem of planning acyclic sequences of contacts, that characterize the motion. In order
to overcome the inherent combinatorial difficulty of this problem, we separate it
in two subproblems: first, planning a guide trajectory for the root of the robot and
then, generating relevant contacts along this trajectory. This paper proposes theo-
retical contributions to these two subproblems. We propose a… Voir plus

Multiped locomotion in cluttered environments is addressed as the prob-
lem of planning acyclic sequences of contacts, that characterize the motion. In order
to overcome the inherent combinatorial difficulty of this problem, we separate it
in two subproblems: first, planning a guide trajectory for the root of the robot and
then, generating relevant contacts along this trajectory. This paper proposes theo-
retical contributions to these two subproblems. We propose a theoretical charac-
terization of the guide trajectory, named “true feasibility”, which guarantees that a
guide can be mapped into the contact manifold of the robot. As opposed to previous
approaches, this property makes it possible to assert the relevance of a guide tra-
jectory without explicitly computing contact configurations. Indeed, this property is
efficiently checked using a low dimensional sampling-based planner (e.g. we imple-
mented a visibility PRM). Since the guide trajectories that we characterize are easily
mapped into a valid sequence of contacts, we then focus on how to select a particular
sequence with desirable properties, such as robustness, efficiency and naturalness,
only considered in cyclic locomotion so far. Based on these novel theoretical de-
velopments, we implement a complete acyclic contact planner and demonstrate its
efficiency by producing a large variety of motions with three very different robots
(humanoid, insectoid, dexterous hand) in five challenging scenarios. The quality of
the obtained motions and the performance of the algorithm make it the first acyclic
contact planner suitable for interactive applications. Voir moins