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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses the problem of synthesizing plausible bipedal locomotion according to 3D anatomical reconstruction and general hypotheses on human motion control strategies. In a previous paper [Nicolas, G., Multon, F., Berillon, G., Marchal, F., 2007. From bone to plausible bipedal locomotion using inverse kinematics. Journal of Biomechanics 40 (5) 1048–1057], we have validated a method based on using inverse kinematics to obtain plausible lower-limb motions knowing the trajectory of the ankle. In this paper, we propose a more general approach that also involves computing a plausible trajectory of the ankles for a given skeleton. The inputs are the anatomical descriptions of the bipedal species, imposed footprints and a rest posture. This process is based on optimizing a reference ankle trajectory until a set of criteria is minimized. This optimization loop is based on the assumption that a plausible motion is supposed to have little internal mechanical work and should be as less jerky as possible. For each tested ankle trajectory, inverse kinematics is used to compute a lower-body motion that enables us to compute the resulting mechanical work and jerk. This method was tested on a set of modern humans (male and female, with various anthropometric properties). We show that the results obtained with this method are close to experimental data for most of the subjects. We also demonstrate that the method is not sensitive to the choice of the reference ankle trajectory; any ankle trajectory leads to very similar result. We finally apply the method to a skeleton of Pan paniscus (Bonobo), and compare the resulting motion to those described by zoologists. [Copyright &y& Elsevier]

Virtual humans are more and more used in VR applications, but their animation is still a challenge, especially if complex tasks must be carried out in interaction with the user. In many applications with virtual humans, credible virtual characters play a major role in presence. Motion editing techniques assume that the natural laws are intrinsically encoded in prerecorded trajectories and that modifications may preserve these natural laws, leading to credible autonomous actors. However, a complete knowledge of all the constraints is required to ensure continuity or to synchronize and blend several actions necessary to achieve a given task. We propose a framework capable of performing these tasks in an interactive environment that can change at each frame, depending on the user's orders. This framework enables VR applications to animate from dozens of characters in real time for complex constraints, to hundreds of characters if only ground adaptation is performed. It offers the following capabilities: motion synchronization, blending, retargeting, and adaptation thanks to enhanced inverse kinetics and kinematics solver. To evaluate this framework, we have compared the motor behavior of subjects in real and in virtual environments. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this study is to validate a method based on anatomical data and biomechanical locomotor hypotheses that could be applied in palaeontology to simulate locomotion in fossil hominids. The main problem is to ensure that purely mathematical simulation, based on anatomical descriptions, is enough to test hypotheses on human motion control. A 3D geometric model of the lower limb was therefore processed from anatomical descriptions. From this 3D model, we developed a method to retrieve natural lower-limb motion depending on chosen constraints. We assumed that the role of lower-limb motion is to make the feet move from one footprint to the next by following a trajectory that resembles that of living humans (primary task). This method based on inverse kinematics also allows biomechanical laws of bipedal locomotion to be taken into account (secondary tasks). The laws tested in this study relate to preserving joint limits, minimizing energy and minimizing the distance to a rest posture proposed by anthropologists and viewed as input to our system. A weighted sum of the resulting derivable cost functions enabled us to select a specific solution in the null space of the primary task. In order to validate this approach, we compared simulated and captured motion from ten subjects for whom anthropometrical data were recorded. We concluded that this “anatomically based bipedalism simulation” seems promising as a means of investigating natural locomotion behaviour and might also be used to retrieve natural locomotion in fossil hominids where only little knowledge is available. [Copyright &y& Elsevier]

This work investigates the design of a new method to evaluate the importance of visual elements taken into account by a handball goalkeeper facing a thrower. Virtual reality was used to design and reproduce standardised situations in a controlled environment. Under such conditions, it was possible to isolate for investigation one visual element in the thrower''s gestures. The goalkeeper''s movements were recorded in order to compare his reactions to two separate throws where only one visual element was modified. Our systems allowed us to measure and record the effects of small changes in the thrower''s movements. With the numerical values we obtained from our results we were able to come up with a scale of significance for each isolated element. These preliminary results look promising for neuroscience, allowing us to better understand the strategies used in duel situations. [Copyright &y& Elsevier]

A number of authors when studying the walk–run transition phenomenon focused either on the mechanical or energy expenditure whilst only a few used both parameters concurrently. Moreover the literature demonstrates that the contribution of these variables changes along with the level and method of training. Consequently the purpose of this study is to find, by analyzing concurrently these two variables, if the walk–run transition speed is linked to the type of training. To this end we calculated two theoretical transition speeds: one based on the metabolic energy expenditure St1 and the second one based on the internal work St2. Subjects were divided into three groups (untrained, sprint and endurance-trained men) who were required to walk and run on a treadmill at increasing speeds. Firstly we show that the relationship between St1 and St2 differs depending on the groups. Sprinters have a significantly lower St2 than St1 whereas the opposite is found for untrained subjects. We also show that the transition speed is linked to the subject’s type of training. To conclude it seems that acquiring running techniques through specific training has consequences for the walk–run transition phenomenon. [Copyright &y& Elsevier]

Virtual reality (VR) has recently seen significant development in interaction with computers and the visualization of information. More and more people are using virtual and immersive technologies in their daily lives, especially for entertainment, fitness, and socializing purposes. This paper presents a qualitative evaluation of a large sample of users using a VR platform for dancing ( N = 292 ); we study the users' motivations, experiences, and requirements for using VR as an inclusive platform for dancing, mainly as a social or physical activity. We used an artificial intelligence platform (OpenAI) to extract categories or clusters of responses automatically. We organized the data into six user motivation categories: fun, fitness, social activity, pandemic, escape from reality, and professional activities. Our results indicate that dancing in virtual reality is a different experience than in the real world, and there is a clear distinction in the user's motivations for using VR platforms for dancing. Our survey results suggest that VR is a tool that can positively impact physical and mental well-being through dancing. These findings complement the related work, help in identifying the use cases, and can be used to assist future improvements of VR dance applications. [ABSTRACT FROM AUTHOR]

Objective: A neural networks approach has been proposed to handle various inputs such as postural, anthropometric and environmental variables in order to estimate self-reported discomfort in picking tasks. An input reduction method has been proposed, reducing the input variables to the minimum data required to estimate self-reported discomfort with similar accuracy as the neural network fed with all variables. Background: Previous works have attempted to explore the relationship between several factors and self-reported discomfort using observational methods. The results showed that this relationship was not a simple linear relationship. Another study used neural networks to model the function returning reported discomfort according to static posture, age, and anthropometrics variables. The results demonstrated the model's ability to predict reported discomfort. But all the available variables were used to design the neural network. Method: Eleven subjects carried-out picking tasks with various masses (0, 1, 3 kg) and imposed duration (5, 10, or 15 s). Continuous REBA score, anthropometric and environmental data were computed, and subjects' discomfort were collected. The data set of this work consisted in the computed continuous REBA score, anthropometric, environmental data and collected subjects' discomfort. Results: The results showed that the correlation between the estimated and experimental tested data was equal to 0.775 when using all the 14 available variables. After data reduction, only 6 variables were left, with a very close performance when predicting discomfort. Conclusion: A neural network approach has been proposed to estimate self-reported discomfort according to a minimum set of postural, anthropometric and environmental variables in picking tasks. Application: This method has the potential to support ergonomists in workstation designing processes, by adding discomfort prediction to virtual manikins' behaviors in simulation tools. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of capturing both the shape and the pose of a human character using a single depth sensor. Some previous works proposed to fit a parametric generic human template into the depth image, while others developed deep learning (DL) approaches to find the correspondence between depth pixels and vertices of the template. We designed a hybrid approach, combining the advantages of both methods, and conducted extensive experiments on the SURREAL Varol et al. (2017), DFAUST datasets Bogo etal (2017) and a subset of AMASS Mahmood et al. (2019). Results show that this hybrid approach enables us to enhance pose and shape estimation compared to using DL or model fitting separately. We also evaluated the ability of the DL-based dense correspondence method to segment also the background — not only the body parts. We also evaluated 4 different methods to perform the model fitting based on a dense correspondence, where the number of available 3D points differs from the number of corresponding template vertices. These two results enabled us to better understand how to combine DL and model fitting, and the potential limits of this approach to deal with real-depth images. Future works could explore the potential of taking temporal information into account, which has proven to increase the accuracy of pose and shape reconstruction based on a unique depth or RGB image. [Display omitted] • Dense correspondence and model fitting enhance 3D shape and pose reconstruction. • Bodypart and background color embedding enables character segmentation in depth map. • Averaging 3D candidate points before model fitting offers the best reconstruction. [ABSTRACT FROM AUTHOR]

In this paper, we present FaceTuneGAN, a new 3D face model representation decomposing and encoding separately facial identity and facial expression. We propose a first adaptation of image-to-image translation networks, that have successfully been used in the 2D domain, to 3D face geometry. Leveraging recently released large face scan databases (FaceScape and CoMA), a neural network has been trained to decouple factors of variations with a better knowledge of the face, enabling facial expressions transfer and neutralization of expressive faces. Specifically, we design an adversarial architecture adapting the base architecture of FUNIT and using SpiralNet++ for our convolutional and sampling operations. Applied on these two datasets, FaceTuneGAN has a better identity decomposition and face neutralization than state-of-the-art techniques. It also outperforms classical deformation transfer approach by predicting blendshapes closer to ground-truth data and with less of undesired artifacts due to too different facial morphologies between source and target. [Display omitted] • 3D face model representation. • Morphology aware expression transfer. • Style based 3D facial generative model. • Semi-supervised learning of 3D identity and expression separation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

• We explore shape transfer, a novel method for motion retargeting. • The method allows to adapt the pose to very different morphologies. • The method is applicable to different types of characters such as humans or animals. We present an automatic method that allows to retarget poses from a source to a target character by transferring the shape of the target character onto the desired pose of the source character. By considering shape instead of pose transfer our method allows to better preserve the contextual meaning of the source pose, typically contacts between body parts, than pose-based strategies. To this end, we propose an optimization-based method to deform the source shape in the desired pose using three main energy functions: similarity to the target shape, body part volume preservation, and collision management to preserve existing contacts and prevent penetrations. The results show that our method allows to retarget complex poses with several contacts to different morphologies, and is even able to create new contacts when morphology changes require them, such as increases in body size. To demonstrate the robustness of our approach to different types of shapes, we successfully apply it to basic and dressed human characters as well as wild animal models, without the need to adjust parameters. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel idea to address the problem of fast computation of stable force-closure grasp configurations for a multifingered hand and a 3-D rigid object represented as a polygonal soup model. The proposed method performs a low-level shape exploration by wrapping multiple cords around the object in order to quickly isolate promising grasping regions. Around these regions, we compute grasp configurations by applying a variant of the close-until-contact procedure to find the contact points. The finger kinematics and the contact information are then used to filter out unstable grasps. Through many simulated examples with three different anthropomorphic hands, we demonstrate that, compared with previous grasp planners such as the generic grasp planner in Simox, the proposed grasp planner can synthesize grasps that are more natural-looking for humans (as measured by the grasp quality measure skewness) for objects with complex geometries in a short amount of time. Unlike many other planners, this is achieved without costly model preprocessing such as segmentation by parts and medial axis extraction. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Recognition of natural gestures is a key issue in many applications including videogames and other immersive applications. Whatever is the motion capture device, the key problem is to recognize a motion that could be performed by a range of different users, at an interactive frame rate. Hidden Markov Models (HMM) that are commonly used to recognize the performance of a user however rely on a motion representation that strongly affects the overall recognition rate of the system. In this paper, we propose to use a compact motion representation based on Morphology-Independent features and we evaluate its performance compared to classical representations. When dealing with 15 very similar upper limb motions, HMM based on Morphology-Independent features yield significantly higher recognition rate (84.9%) than classical Cartesian or angular data (70.4% and 55.0%, respectively). Moreover, when the unknown motions are performed by a large number of users who have never contributed to the learning process, the recognition rate of Morphology-Independent input feature only decreases slightly (down to 68.2% for a HMM trained with the motions of only one subject) compared to other features (25.3% for Cartesian features and 17.8% for angular features in the same conditions). The method is illustrated through an interactive demo in which three virtual humans have to interactively recognize and replay the performance of the user. Each virtual human is associated with a HMM recognizer based on the three different input features. [ABSTRACT FROM AUTHOR]

Virtual reality has a number of advantages for analyzing sports interactions such as the standardization of experimental conditions, stereoscopic vision, and complete control of animated humanoid movement. Nevertheless, in order to be useful for sports applications, accurate perception of simulated movement in the virtual sports environment is essential. This perception depends on parameters of the synthetic character such as the number of degrees of freedom of its skeleton or the levels of detail (LOD) of its graphical representation. This study focuses on the influence of this latter parameter on the perception of the movement. In order to evaluate it, this study analyzes the judgments of immersed handball goalkeepers that play against a graphically modified virtual thrower. Five graphical representations of the throwing action were defined: a textured reference level (L0), a nontextured level (L1), a wire-frame level (L2), a moving point light display (MLD) level with a normal-sized ball (L3), and a MLD level where the ball is represented by a point of light (L4). The results show that judgments made by goalkeepers in the L4 condition are significantly less accurate than in all the other conditions (p < .001). This finding means that the goalkeepers' perception of the movement is influenced more by the size of the ball during the judgment task than the graphical LOD of the throwing action. The MLD representation of the movement thus appears to be sufficient for a sports duel analysis in virtual environments. [ABSTRACT FROM AUTHOR]

Setups that integrate both kinematics and morpho-functional investigations of a single sample constitute recent developments in the study of nonhuman primate bipedalisms. We introduce the integrated setup built at the Primatology Station of the French National Centre for Scientific Research (CNRS), which allows analysis of both bipedal and quadrupedal locomotion in a population of 55–60 captive olive baboons. As a first comparison, we present the hind limb kinematics of both locomotor modalities in 10 individuals, focusing on the stance phase. The main results are: 1) differences in bipedal and quadrupedal kinematics at the hip, knee, and foot levels; 2) a variety of foot contacts to the ground, mainly of semiplantigrade type, but also of plantigrade type; 3) equal variations between bipedal and quadrupedal foot angles; 4) the kinematics of the foot joints act in coordinated and stereotyped manners, but are triggered differently according to whether the support is bipedal or quadrupedal. Although very occasionally realized, the bipedal walk of olive baboon appears to be a habitual and nonerratic locomotor modality. [ABSTRACT FROM AUTHOR]

The article provides information on improving sports performance through the use of virtual reality. It states that video playback on past performances of players is limited because the camera is focused on an area. Through the use of virtual reality, numerical stimulations and immersive, interactive environments can be realized. To better estimate performances, a video-game technology has been designed to detect a perception-only task and a perception-action task.

The authors investigated how different levels of detail (LODs) of a virtual throwing action can influence a handball goalkeeper's motor response. Goalkeepers attempted to stop a virtual ball emanating from five different graphical LODs of the same virtual throwing action. The five levels of detail were: a textured reference level (L0), a non-textured level (L1), a wire-frame level (L2), a point-light-display (PLD) representation (L3) and a PLD level with reduced ball size (L4). For each motor response made by the goalkeeper we measured and analyzed the time to respond (TTR), the percentage of successful motor responses, the distance between the ball and the closest limb (when the stopping motion was incorrect) and the kinematics of the motion. Results showed that TTR, percentage of successful motor responses and distance with the closest limb were not significantly different for any of the five different graphical LODs. However the kinematics of the motion revealed that the trajectory of the stopping limb was significantly different when comparing the L1 and L3 levels, and when comparing the L1 and L4 levels. These differences in the control of the goalkeeper's actions suggests that the different level of information available in the PLD representations (L3 and L4) are causing the goalkeeper to adopt different motor strategies to control the approach of their limb to stop the ball. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Virtual reality offers new tools for human motion understanding. Several applications have been widely used in teleoperation, military training, driving and flying simulators, and so forth. We propose to test if virtual reality is a valid training tool for the game of handball. We focused on the duel between a handball goalkeeper and a thrower. To this end, we defined a pilot experiment divided into two steps: an experiment with real subjects and another one with virtual throwers. The throwers' motions were captured in order to animate their avatar in a reality center. In this paper, we focused on the evaluation of presence when a goalkeeper is confronting these avatars. To this end, we compared the goalkeeper's gestures in the real and in the virtual experiment to determine if virtual reality engendered the same movements for the same throw. Our results show that gestures did not differ between the real and virtual environment. As a consequence, we can say that the virtual environment offered enough realism to initiate natural gestures. Moreover, as in real games, we observed the goalkeeper's anticipation to allow us to use virtual reality in future work as a way to understand the goalkeeper and thrower interactions. The main originality of this work was to measure presence in a sporting application with new evaluation methods based on motion capture. [ABSTRACT FROM AUTHOR]