Evaluating potential musculoskeletal disorders risks in real workstations is challenging as the environment is cluttered, which makes it difficult to accurately assess workers' postures. Being marker-free and calibration-free, Microsoft Kinect is a promising device although it may be sensitive to occlusions. We propose and evaluate a RULA ergonomic assessment in real work conditions using recently published occlusion-resistant Kinect skeleton data correction. First, we compared postures estimated with this method to ground-truth data, in standardized laboratory conditions. Second, we compared RULA scores to those provided by two professional experts, in a non-laboratory cluttered workplace condition. The results show that the corrected Kinect data can provide more accurate RULA grand scores, even under sub-optimal conditions induced by the workplace environment. This study opens new perspectives in musculoskeletal risk assessment as it provides the ergonomists with 30 Hz continuous information that could be analyzed offline and in a real-time framework.

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.

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.

International audienceJoint torques and forces are relevant quantities to estimate the biomechanical constraints of working tasks in ergonomics. However, inverse dynamics requires accurate motion capture data, which are generally not available in real manufacturing plants. Markerless and calibrationless measurement systems based on depth cameras, such as the Microsoft Kinect, are promising means to measure 3D poses in real time. Recent works have proposed methods to obtain reliable continuous skeleton data in cluttered environments, with occlusions and inappropriate sensor placement. In this paper, we evaluate the reliability of an inverse dynamics method based on this corrected skeleton data and its potential use to estimate joint torques and forces in such cluttered environments. To this end, we compared the calculated joint torques with those obtained with a reference inverse dynamics method based on an optoelectronic motion capture system. Results show that the Kinect skeleton data enabled the inverse dynamics process to deliver reliable joint torques in occlusion-free (r=0.99 for the left shoulder elevation) and occluded (r=0.91 for the left shoulder elevation) environments. However, differences remain between joint torques estimations. Such reliable joint torques open appealing perspectives for the use of new fatigue or solicitation indexes based on internal efforts measured on site.Relevance to industry: The study demonstrates that corrected Kinect data could be used to estimate internal joint torques, using an adapted inverse dynamics method. The method could be applied on-site because it can handle some cases with occlusions. The resulting Kinect-based method is easy-to-use, real-time and could assist ergonomists in risk evaluation on site

Lunge motion is one of the fundamental attacks used in modern fencing, asking for a high level of coordination, speed and accuracy to be efficient. The aim of the current paper was the assessment of fencer’s performance and response time in lunge attacks under uncertain conditions. For this study, an innovative fencing lunge simulator was designed. The performance of 11 regional to national-level fencers performing lunges in Fixed, Moving and Uncertain conditions was assessed. The results highlighted notably that i) Accuracy and success decreased significantly in Moving and Uncertain conditions with regard to Fixed ones ii) Movement and Reaction times were also affected by the experimental conditions iii) Different fencer profiles were distinguishable among subjects. In conclusion, the hypothesis that fencers may privilege an adaptation to the attack conditions and preserve accuracy instead of privileging quickness was supported by the results. Such simulators may be further used to analyze in more detail the motor control strategies of fencers through the measure and processing of biomechanical quantities and a wider range of fencing levels. It has also a great potential to be used as training device to improve fencer’s performance to adapt his attack to controlled opponent’s motion.

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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.