According to the demographic evolution in industrialized countries, more and more elderly people will experience falls at home and will require emergency services. The main problem comes from fall-prone elderly living alone at home. To resolve this lack of safety, we propose a new method to detect falls at home, based on a multiple-cameras network for reconstructing the 3-D shape of people. Fall events are detected by analyzing the volume distribution along the vertical axis, and an alarm is triggered when the major part of this distribution is abnormally near the floor during a predefined period of time, which implies that a person has fallen on the floor. This method was validated with videos of a healthy subject who performed 24 realistic scenarios showing 22 fall events and 24 cofounding events (11 crouching position, 9 sitting position, and 4 lying on a sofa position) under several camera configurations, and achieved 99.7% sensitivity and specificity or better with four cameras or more. A real-time implementation using a graphic processing unit (GPU) reached 10 frames per second (fps) with 8 cameras, and 16 fps with 3 cameras.

Improving performance in sports can be difficult because many biomechanical, physiological, and psychological factors come into play during competition. A better understanding of the perception-action loop employed by athletes is necessary. This requires isolating contributing factors to determine their role in player performance. Because of its inherent limitations, video playback doesn't permit such in-depth analysis. Interactive, immersive virtual reality (VR) can overcome these limitations and foster a better understanding of sports performance from a behavioral-neuroscience perspective. Two case studies using VR technology and a sophisticated animation engine demonstrate how to use information from visual displays to inform a player's future course of action.

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.

Previous studies on overarm throwing have described a proximal-to-distal segmental sequence. The proximal segments reached their maximal linear velocities before the distal ones. In handball, no study has demonstrated this sequence from the upper torso to the wrist, although a recent study did present a different organization. The aim of this study was to analyse the throwing arm segmental organization during handball throwing. We found that the maximal linear velocity of the shoulder occurred after the maximal linear velocity of the elbow. Moreover, the maximal angular velocity of the upper torso occurred later than that of the elbow. Hence, contrary to other disciplines, the rotation of the upper torso was not suddenly stopped just after the forward arm motion was initiated. These results may apply to handball in general or be specific to the population of handball players studied. It may be advisable in future studies to include international players.