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posture-measurement
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Marker-less versus marker-based driven musculoskeletal models of the spine during static load-handling activities
, Article Journal of Biomechanics ; Volume 112 , 2020 ; 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...
Improved artificial neural networks for 3D body posture and lumbosacral moment predictions during manual material handling activities
, Article Journal of Biomechanics ; Volume 131 , 2022 ; 00219290 (ISSN) ; Aghazadeh, F ; Arjmand, N ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
Body posture measurement approaches, required in biomechanical models to assess risk of musculoskeletal injuries, are usually costly and/or impractical for use in real workplaces. Therefore, we recently developed three artificial neural networks (ANNs), based on measured posture data on several individuals, to predict whole body 3D posture (coordinates of 15 markers located on body's main joints), segmental orientations (Euler angles of 14 body segments), and lumbosacral (L5-S1) moments during static manual material handling (MMH) activities (ANNPosture, ANNAngle, and ANNMoment, respectively). These ANNs require worker's body height, body weight (only for ANNMoment), hand-load 3D position,...