Pierre Plantard

Pose estimation with a Kinect for ergonomic studies: evaluation of the accuracy using a virtual mannequin.

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.

Filtered pose graph for efficient kinect pose reconstruction

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.

Usability of corrected Kinect measurement for ergonomic evaluation in constrained environment

Evaluation of potential risks of musculoskeletal disorders in real workstations is challenging as the environment is cluttered, which makes it difficult to correctly assess the pose of a worker. Being marker-free and calibration-free, Microsoft Kinect is a promising device to assess these poses, but it can deliver unreliable poses especially when occlusions occur. To overcome this problem, we propose to detect and correct badly recognised body parts thanks to a database of example poses. We applied the proposed method to compute rapid upper limb assessment (RULA) score in a realistic environment that involved sub-optimal Kinect placement and several types of occlusions. Results showed that when occlusions occur, the inaccurate raw Kinect data could be significantly improved using our correction method, leading to acceptable joint angles and RULA scores. Our method opens new perspectives to define new fatigue or solicitation indexes based on continuous measurement contrary to classical static images used in ergonomics.

Motion Adaptation in Fencing Lunges: a Pilot Study

Fencing is an Olympic sport composed of three different weapons (foil, sabre and épée), with specific rules and target areas. This open-skilled sport involves high levels of neuromuscular coordination, strength and power. To score against their opponent during attacks, one of the most frequent action used is the lunge. This technical action requires an explosive extension of the trailing leg to perform a forceful move forward of the weapon in order to quickly touch their opponent. During this attack, the fencer makes certain adjustments to change the goal according to the response of the opponent. The complexity of the visual stimulus selection lead to a decrease of the attack performance (Sanderson 1983). The impact of the stimulus complexity selection is mainly assessed through the notion of uncertainty (Gutiérrez-Dávila et al. 2014), especially the effect of target change during the attack (Gutiérrez-Dávila et al. 2013). Adjustments are prone to appear at different descriptive levels of motor control, especially at the muscular level. The goal of this pilot study is to evaluate the adaptation of the fencer’s muscle synergies during the attack, when visual perturbations occur.