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    Prediction of Lumbar Muscle Forces Due to Applied Externally Moment and Analaysing Coactivation Between Lumbar Muscles

    , M.Sc. Thesis Sharif University of Technology Khodadad Kani, Hadi (Author) ; Parnianpour, Mohammad (Supervisor) ; Shams Al-Allahi, Mohammad Bagher (Co-Advisor)
    Abstract
    The role of the lumbar muscles in maintaining posture and balancing externally applied loads has been studied for many years.preliminary studies used electromyograpy (EMG) to establish a relationship between trunk posture and muscle ativity.so developing a better undrestanding of the functional role of those muscles. Determination of lumbar muscle forces, will have an important role in reducing low back pain I future.For this reason,reaserchers have done many studies for understanding the resistence and function on the musulosketal system bye using of various protocls and have utilized of various biomechanical models for determining the effects of applied externally loads on lumbar... 

    Development of 3D Upper Body Posture Prediction Model for Static Lifting at Standing Posture

    , M.Sc. Thesis Sharif University of Technology Abrishamkar, Amin (Author) ; Arjmand, Navid (Supervisor) ; Zohoor, Hassan (Co-Advisor)
    Abstract
    One of the main causes of low back pain is mechanical loads acting on it. These loads can be measured by in vivo measurement using force and pressure sensors. Although this method leads to accurate results, it is invasive and costly. Currently the only noninvasive way to estimate these loads is to utilize biomechanical models. Using these models requires body posture as an input. Imaging techniques are suitable for determining the posture accurately, but these equipments are expensive and often impractical. So today posture prediction models are used as an alternative cost effective with acceptable accuracy. These mechanisms predict body posture using body mass, height, position and the... 

    3D Reconstruction of Human Pose in Multi-View Dynamic Scenes

    , Ph.D. Dissertation Sharif University of Technology Ershadi Nasab, Sara (Author) ; Sanaei, Esmaeil (Supervisor) ; Kasaei, Shohreh (Co-Advisor)
    Abstract
    In this thesis, 3D pose reconstruction of one or multiple humans in multi-view dynamic scene is considered. Inputs are multi-view frames of multi-view camera systems. Outputs are the 3D reconstructed human poses in 3D space. The pose is the location of 14 human body joints in the 3D space. In this research, it is not allowed to use Kinect sensor data or other Markers or GPS sensors. It is supposed that only multi-view images are used. 3D reconstruction of human body pose can be performed with different assumptions. For example, the scene is indoor only or the camera calibration information is provided at first.Cameras are moving or fixed. In this research, each of this assumption is regarded... 

    3D Spinal Kinematics During Load-Handling Activities, Range of Motions and Movement Coordination in Normal and Obese Individuals

    , M.Sc. Thesis Sharif University of Technology Ghasemi Varnamkhasti, Morteza (Author) ; Arjmand, Navid (Supervisor)
    Abstract
    Today, obesity, as a major global health challenge, affects more than 30 percent of the world's population. To investigate the effect of obesity on spinal function, a common method is motion analysis (kinematic method). This method is based on the claim that the abnormal mechanical function of the spine is directly related to its abnormal motions.The aim of this study is to measure and compare the range of motions (RoMs) of different segments of the spine in all anatomical plates between obese and normal individuals, as well as to calculate and compare some motion rhythms between the lumbar spine and the pelvis in these two groups. Comparing the posture of the spine between obese and normal... 

    A Dynamic Network Approach to Inertial Motion Capture

    , Ph.D. Dissertation Sharif University of Technology Razavi, Hamid Reza (Author) ; Alasty, Aria (Supervisor) ; Salarieh, Hassan (Co-Supervisor)
    Abstract
    The current study introduces algorithms for inertial motion capture which use data from 9-DOF inertial-magnetic sensor modules to estimate the position and attitude of body links. First, an algorithm is proposed which is capable of IMU calibration without the use of external equipment with less than 0.5% error. Next, extended and unscented Kalman filter-based (EKF and UKF) inertial motion capturing algorithms are introduced that utilize biomechanical constraints in addition to kinematics. In addition to real-time sensor calibration, the algorithms are capable of real-time link geometry estimation, which allows for the imposition of biomechanical constraints without a priori knowledge...