With the wide spread of affordable virtual reality headsets, virtual environments are rapidly changing the way humans interact with reality. Understanding the effects of virtual environments on the mental and cognitive state is essential. In addition, defining methods for measuring and assessing visual fatigue in virtual environments is still needed. While eye movements is tightly coupled to the mental state, analysis of eye movement can add insights for safer virtual environments. Biomechanical analysis has been used extensively in the analysis of human movement. Simulation of different scenarios like injuries and surgeries provided insights and solutions to problems that were otherwise impossible. This includes understanding the effects of changing insertion points of muscle on range of motion or how muscle activation can affect the motion produced. Extending the use of biomechanical simulation analysis into eye movement can be used to deepen our understanding of how virtual environments affects our visual and mental capabilities. This paper presents a thorough review on ocular biomechanics and ocular models in literature. We start with a brief introduction on the anatomy of the eye and eye kinematics. In addition, properties of the extraocular muscles (EOM) are described and the difference between EOMs and skeletal muscle is highlighted. The challenges facing biomechanical simulation and analysis of eye movement are presented along with the role of ocular models in assessing visual fatigue. Furthermore, the compatibility of available biomechanical tools to analyse ocular movements is discussed.

Human posture analysis is a task of utmost importance for several disciplines. For ergonomists, extracting postural information such as joint angles is necessary to evaluating ergonomie assessment metrics. This allows the early identification of potential work-related musculoskeletal disorders in manufacturing industries, and thus providing adequate interventions. In this paper, we present a holistic posture analysis system that estimates body joint angles from an input depth image. The proposed method utilizes the low cost Kinect sensor for data acquisition and a deep convolutional neural network model for joint angles regression. Further, we rely on learning from synthetic training images to allow simulating several physical tasks by different workers and obtain a highly generalizable learning model. The corresponding ground truth joint angles have been generated using a novel inverse kinematic stage. The proposed method achieves high joint angels prediction rate by recording an average MAE of 4.67 deg and RMSE of 6.64 deg.

Human behavior understanding is a well-known area of interest for computer vision researchers. This discipline aims at evaluating several aspects of interactions among humans and system components to ensure long term human well-being. The robust human posture analysis is a crucial step towards achieving this target. In this paper, the deep representation learning paradigm is used to analyze the articulated human posture and assess the risk of having work-related musculoskeletal discomfort in manufacturing industries. Particularly, we train a deep residual convolutional neural network model to predict body joint angles from a single depth image. Estimated joint angles are essential for ergonomists to evaluate ergonomic assessment metrics. The proposed method applies the deep residual learning framework that has demonstrated impressive convergence speed and generalization capabilities in addressing different vision tasks such as object recognition, localization and detection. Moreover, we extend the state-of-the-art data generation pipeline to synthesize a dataset that features simulations of manual tasks performed by different workers. An inverse kinematics stage is proposed to generate the corresponding ground truth joint angles. Experimental results demonstrate the generalization performance of the proposed method.