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    Inertial motion capture accuracy improvement by kalman smoothing and dynamic networks

    , Article IEEE Sensors Journal ; Volume 21, Issue 3 , 2021 , Pages 3722-3729 ; 1530437X (ISSN) Razavi, H ; Salarieh, H ; Alasty, A ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2021
    Abstract
    Localization-capable inertial motion capture algorithms rely on zero-velocity updates (ZUPT), usually as measurements in a Kalman filtering scheme, for position and attitude error control. As ZUPTs are only applicable during the static phases a link goes through, estimation errors grow during dynamic ones. This error growth may somewhat be mitigated by imposing biomechanical constraints in multi-sensor systems. Error reduction is also possible by optimization-based methods that incorporate the dynamic and static constraints governing the system behavior over a period of time (e.g. the dynamic network algorithm); when this period includes multiple static phases for a link, its estimation... 

    Towards real-time partially self-calibrating pedestrian navigation with an inertial sensor array

    , Article IEEE Sensors Journal ; Volume 20, Issue 12 , 2020 , Pages 6634-6641 Razavi, H ; Salarieh, H ; Alasty, A ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2020
    Abstract
    Inspired by algorithms utilized in inertial navigation, an inertial motion capturing algorithm capable of position and heading estimation is introduced. The fusion algorithm is capable of real-time link geometry estimation, which allows for the imposition of biomechanical constraints without a priori knowledge regarding sensor placements. Furthermore, the algorithm estimates gyroscope and accelerometer bias, scaling, and non-orthogonality parameters in real-time. The stationary phases of the links, during which pseudo-measurements such as zero velocity or heading stabilization updates are applied, are detected using optically trained neural networks with buffered accelerometer and gyroscope... 

    Marker-less versus marker-based driven musculoskeletal models of the spine during static load-handling activities

    , Article Journal of Biomechanics ; Volume 112 , 2020 Asadi, F ; Arjmand, N ; Sharif University of Technology
    Elsevier Ltd  2020
    Abstract
    Evaluation of workers’ body posture in workstations is a prerequisite to estimate spinal loads and assess risk of injury for the subsequent design of preventive interventions. The Microsoft Kinect™ sensor is, in this regard, advantageous over the traditional skin-marker-based optical motion capture systems for being marker-less, portable, cost-effective, and easy-to-use in real workplaces. While several studies have demonstrated the validity/reliability of the Kinect for posture measurements especially during gait trials, its capability to adequately drive a detailed spine musculoskeletal model for injury risk assessments remains to be investigated. Lumbosacral (L5-S1) load predictions of a...