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Muscle and joint forces
Published in Paul Grimshaw, Michael Cole, Adrian Burden, Neil Fowler, Instant Notes in Sport and Exercise Biomechanics, 2019
In dynamics we utilise the acceleration approach to solve problems. Also, remember from Figure D1.1 that inverse dynamics is the computation of muscle and joint forces from kinematics (displacement, velocity and acceleration). Hence, the first and second conditions of equilibrium, which we used for the statics calculations, are now represented as the following dynamic equations. ∑Fx=max∑Fy=may∑M=Iα
Modelling in Rowing
Published in Youlian Hong, Routledge Handbook of Ergonomics in Sport and Exercise, 2013
The system (6) can be analysed by two forms: direct or inverse dynamics (Winter, 1990). Direct dynamics analysis is concerned with obtaining the response over time of the multi-body system when driven by loads. It is supposed to be known the moments of force that are realized across each biomechanical joint, and one looks up to calculate the angles that determine the motion, through the resolution of the system of ordinary differential equations of the second order. This resolution of mathematical interest has no such interest on the clinical point of view, because the amplitudes of the angles can be measured by non-invasive methods whereas the same does not happen with the moments of force. The objective of the inverse dynamics analysis is to calculate the moments of force that result from the muscle activity about the joints, which is responsible by the performed motion. For the determination of the moments of force, the angles are supposed to be known, as well as respective velocities and accelerations, which determine the given trajectories. Then, the necessary muscle control can be identified. Effectively, the direct resolution is useful to create a standard of reference with which the physiatrists or the physiotherapists can compare and determinate the disorders. Consequently, this analysis may be valuable in the diagnostic of pathologies and, in particular, the clinical state of determinate organ.
Human–Machine Force Interactive Interface and Exoskeleton Robot Techniques Based on Biomechanical Model of Skeletal Muscle
Published in Yuehong Yin, Biomechanical Principles on Force Generation and Control of Skeletal Muscle and their Applications in Robotic Exoskeleton, 2020
Dynamic model analysis of the exoskeleton robot aims to get the required torque and power of joint motion, and to select appropriate motor parameters so as to drive the load, and also provide theoretical foundations for the active control of robot. The forward dynamics is to solve the motion of exoskeleton, including the joint displacement, velocity, and acceleration based on the driving force/torque of every joint. It is mainly used for system simulation. The inverse dynamics aims to solve the required joint force/torque based on the known joint displacement, velocity and acceleration, and system’s mass inertia. It can be used for actuator selection and real-time control of the robot [110]. There are two methods to develop the robot’s dynamic model: the Newton–Euler method and the Lagrangian mechanics. When we use Newton–Euler function, we have to get the acceleration based on the kinematics and develop models for every component, the computation of which is complex. Lagrangian mechanics is used here to obtain the system’s dynamic feature without calculating the internal force between the components. The Lagrangian function is Fi=ddt∂L∂q˙i−∂L∂qi,i=1,2,⋯,n
Adaptive state-feedback stabilisation of output-constrained high-order nonlinear systems with iISS inverse dynamics
Published in International Journal of Control, 2023
Due to the limited measurement techniques and modelling approaches, the inverse dynamics (also called dynamic uncertainty) always exists in nonlinear systems and makes the control task more difficult and complicated. To deal with the inverse dynamics in different kinds of nonlinear systems, some original works (Jiang, 1999; Jiang & Mareels, 1997, 2001; Praly & Jiang, 1993) were obtained. However, the considered inverse dynamics in Jiang (1999), Jiang and Mareels (1997, 2001), and Praly and Jiang (1993) are all based on the assumption of input-to-state stability (ISS). In Angeli et al. (2000), Arcak et al. (2002), and Sontag (1998), another important concept named integral input-to-state stability (iISS), which was proved to be strictly weaker than ISS, was proposed for characterising the unmeasurable inverse dynamics. In Jiang et al. (2004), the global control of nonlinear systems with iISS inverse dynamics was firstly discussed in a unified framework. Subsequently, Xie et al. (2011) extended the properties of iISS inverse dynamics to high-order nonlinear systems. However, the above-mentioned results of iISS inverse dynamics are not applicable to nonlinear systems with the output constraint.
Inverse dynamics, joint reaction forces and loading in the musculoskeletal system: guidelines for correct mechanical terms and recommendations for accurate reporting of results
Published in Sports Biomechanics, 2021
Biomechanical analysis of human motion usually involves the representation of the human body as a system of interconnected rigid bodies as the mechanics of deformable bodies are too complex. Inverse dynamics is the computational technique that is based on the equations of motion describing the mechanics of a rigid body to calculate the forces and moments acting on the joints and other structures when the kinematics of the rigid body motion and any external forces are known. We typically measure the translational and rotational kinematics with motion analysis systems and measure any external forces, for example, the ground reaction forces, using force plates, and then utilise the equations of motion in the inverse direction since the kinematics are known and we calculate the forces and moments required for generating the observed motion. This requires a number of simplifications to be able to represent a biological system and the complex anatomy of a human segment with a mechanical rigid body model. These modelling simplifications result in a FBD for each segment which is the rigid body model and the forces and moments acting on it described in a relevant reference frame linked to the chosen coordinate system (Nigg & Herzog, 2007). The equations of motion for the FBD are usually formulated using the Newton-Euler method (Derrick et al., 2019) and they can be used to explore the dynamics of the modelled mechanical system and calculate musculoskeletal loading through inverse dynamics.
Effect of tendon length in the estimation of musculotendon forces during an elbow flexion-extension
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2019
O. Jemaa, S. Bennour, D. Daney, L. Romdhane
The biomechanical study of muscle forces is one of the major topics in several fields such as ergonomics, medicine, sports and rehabilitation. In general, two traditional approaches are used to estimating the muscle forces: inverse dynamics and forward dynamics (Erdemir et al. 2007). Inverse dynamics is an approach that utilized known kinematic data and external forces to calculate the muscle forces (Davoudabadi et al. 2015). Direct dynamics is an approach, which uses the EMG muscular signals to estimate muscle forces and joints moments(Bennour et al. 2013). However, the human body is a redundant system where the number of muscles used to actuate each joint usually higher than its degrees of freedom of freedom. Another challenge comes from the difficulty to quantify the muscle forces due to the variability of the geometric parameters required to describe the biomechanical model of the body. In this work, we are developed a robust and computationally efficient methodology to estimate musculotendon forces from kinematic data and EMG measurements during dynamic contraction to take into consideration the variability of the geometric parametersof muscles. Specifically, this approach consists to study the influence of properties muscles, e.g. tendon length, on musculotendon forces.