Predicting the influence of hip and lumbar flexibility on lifting motions using optimal control

Abstract

Computational models of the human body coupled with optimization can be used to predict the influence of variables that cannot be experimentally manipulated. Here, we present a study that predicts the motion of the human body while lifting a box, as a function of flexibility of the hip and lumbar joints in the sagittal plane. We modeled the human body in the sagittal plane with joints actuated by pairs of agonist-antagonist muscle torque generators, and a passive hamstring muscle. The characteristics of a stiff, average and flexible person were represented by co-varying the lumbar range-of-motion, lumbar passive extensor-torque and the hamstring passive muscle-force. We used optimal control to solve for motions that simulated lifting a 10 kg box from a 0.3 m height. The solution minimized the total sum of the normalized squared active and passive muscle torques and the normalized passive hamstring muscle forces, over the duration of the motion. The predicted motion of the average lifter agreed well with experimental data in the literature. The change in model flexibility affected the predicted joint angles, with the stiffer models flexing more at the hip and knee, and less at the lumbar joint, to complete the lift. Stiffer models produced similar passive lumbar torque and higher hamstring muscle force components than the more flexible models. The variation between the motion characteristics of the models suggest that flexibility may play an important role in determining lifting technique.

Optimization-based biomechanical lifting models for manual material handling: A comprehensive review

“…Gundogdu et al 27 used a dynamic-joint-strength-based 2D model to compute the moment and joint load for different lifting motions. Sreenivasa et al 31 proposed a 12-DOF 2D model to study the influence of hip and lumbar flexibility during lifting motion, where dynamic joint strength was used for the skeletal model. In addition, the direct shooting method was used to find the optimal lifting motion in this study.…”

“…Therefore, motion predictions are good for dynamic, fast, and propulsive operations such as walking, running, jumping, and box lifting tasks. 26,27,[29][30][31]35,39,40,60…”

“…27,28 Genetic algorithms (GA) and gradient-based sequential quadratic programming (SQP) algorithms were used to solve large-scale optimization-based lifting motion prediction problems. 27 Various human performance measures were tested, such as dynamic effort, 26,27,29 motion smoothness, 30 muscle activation, 31 spine loads, 32 stability 29 or a combination of these objective functions using a multi-objective optimization (MOO) technique. 29,30 Since the lifting motion involves the whole body, many strategies are possible to generate a lifting trajectory for the same initial and final positions.…”

Hybrid musculoskeletal model-based 3D asymmetric lifting prediction and comparison with symmetric lifting

“…24 The simplest biomechanical models are 2D models, which are computationally efficient. 20,24,[29][30][31][32] The National Institute of Occupational Safety and Health (NIOSH) proposed a lifting equation which later became a standard for evaluating a lifting task in commercial workspaces. 33,34 This is a regression-based 2D model and does not contain any muscle physiology and spinal shear stress in its evaluation procedure.…”

“…[40][41][42][43][44] Gu¨ndogdu et al 24 developed a dynamic-joint-strength-based 2D biomechanical model to study the joint moment and load during lifting. Sreenivasa et al 31 proposed a dynamic-joint-strength-based model to study the influence of hip and lumbar flexibility during lifting motion. Xiang et al 11 developed a 10-DOF dynamic-jointstrength-based model which can predict lifting motion.…”