Whole-body movement modeling in realistic environments for understanding performance and injury

Harrison, Simon; Cohen, Raymond C.Z.; Cleary, Paul W.

doi:10.1016/b978-0-12-823913-1.00021-x

Cited by 2 publications

(1 citation statement)

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“…Previously we presented a software called Ergomechanic (Cohen and Harrison 2021) which has been used to develop human digital twins (HDTs) (Harrison and Cohen 2021). Ergomechanic uses markerless motion capture (MMC) (Harrison et al 2023) to non-invasively measure body size, pose and movement using cameras, computer vision and biomechanical modelling, but requiring no wearable items placed on the body (active or passive). The full non-invasiveness of this method enables its use in places such as the workplaces, the home, the sports field, or the hospital without affecting the participants' movement in any manner.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of fast approximate inverse kinematics for digital twin models of human movement

2023

MODSIM2023, 25th International Congress on Modelling and Simulation.

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Digital twin models of human movement can be used to enhance physical performance, improve health, and reduce injury risk. Non-invasive motion capture can be used to measure body dimensions and body movement. When combined with biomechanical modelling and domain specific analyses, such as for ergonomics, these models can be used to estimate how changes to activity and workplace design correlate to performance and injury risk.We have developed a prototype digital twin system called Ergomechanic that measures body movement and body segment loading but not all features are currently available in a real-time application. Joint angles are used in biomechanics to compare between study participants and evaluate differences from mean results. This is because these angles are relatively invariant between the population, despite large variations in height and weight. Inverse kinematics (IK) is the process of fitting a biomechanical model to measured movements of a person, though which joint angles can be calculated. As this process relies on a large sequence of recursive equations and a least-squares optimisation approach, it takes too long to converge, and can be too slow for realtime system applications. Here we call this method the "SlowIK" approach.Here we propose a simple and fast approximation to the IK problem (called "FastIK"). It reduces the complexity of the applied equations and is explicit in formulation, thus avoids the optimisation formulation. The method is implemented in the Ergomechanic pipeline and tested with both a simple walking motion and a more complex workout activity. The FastIK method is more than 100x faster than the SlowIK method for both test activities. The knee joint angles calculated by the FastIK approach are found to be within a suitable accuracy in comparison to the SlowIK method, for most biomechanics applications. The elbow joint angle is reproduced well for the FastIK case in two of four cases but has a systematic offset in one instance and incorrectly identifies the sign of the angle in a second instance. This method shows good potential for reasonable accuracy of digital twin outputs when subject to the challenges of real-time applications.

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Section: Introductionmentioning

confidence: 99%