Dataset Information

Efficient trajectory optimization for curved running using a 3D musculoskeletal model with implicit dynamics.

ABSTRACT: Trajectory optimization with musculoskeletal models can be used to reconstruct measured movements and to predict changes in movements in response to environmental changes. It enables an exhaustive analysis of joint angles, joint moments, ground reaction forces, and muscle forces, among others. However, its application is still limited to simplified problems in two dimensional space or straight motions. The simulation of movements with directional changes, e.g. curved running, requires detailed three dimensional models which lead to a high-dimensional solution space. We extended a full-body three dimensional musculoskeletal model to be specialized for running with directional changes. Model dynamics were implemented implicitly and trajectory optimization problems were solved with direct collocation to enable efficient computation. Standing, straight running, and curved running were simulated starting from a random initial guess to confirm the capabilities of our model and approach: efficacy, tracking and predictive power. Altogether the simulations required 1 h 17 min and corresponded well to the reference data. The prediction of curved running using straight running as tracking data revealed the necessity of avoiding interpenetration of body segments. In summary, the proposed formulation is able to efficiently predict a new motion task while preserving dynamic consistency. Hence, labor-intensive and thus costly experimental studies could be replaced by simulations for movement analysis and virtual product design.

SUBMITTER: Nitschke M

PROVIDER: S-EPMC7573630 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Efficient trajectory optimization for curved running using a 3D musculoskeletal model with implicit dynamics.

Nitschke Marlies M Dorschky Eva E Heinrich Dieter D Schlarb Heiko H Eskofier Bjoern M BM Koelewijn Anne D AD van den Bogert Antonie J AJ

Scientific reports 20201019 1

Trajectory optimization with musculoskeletal models can be used to reconstruct measured movements and to predict changes in movements in response to environmental changes. It enables an exhaustive analysis of joint angles, joint moments, ground reaction forces, and muscle forces, among others. However, its application is still limited to simplified problems in two dimensional space or straight motions. The simulation of movements with directional changes, e.g. curved running, requires detailed t ...[more]

PMID: 33077752

Similar Datasets

Project description:From the computational point of view, musculoskeletal control is the problem of controlling high degrees of freedom and dynamic multi-body system that is driven by redundant muscle units. A critical challenge in the control perspective of skeletal joints with antagonistic muscle pairs is finding methods robust to address this ill-posed nonlinear problem. To address this computational problem, we implemented a twofold optimization and learning framework to be specialized in addressing the redundancies in the muscle control . In the first part, we used model predictive control to obtain energy efficient skeletal trajectories to mimick human movements. The second part is to use deep reinforcement learning to obtain a sequence of stimulus to be given to muscles in order to obtain the skeletal trajectories with muscle control. We observed that the desired stimulus to muscles is only efficiently constructed by integrating the state and control input in a closed-loop setting as it resembles the proprioceptive integration in the spinal cord circuits. In this work, we showed how a variety of different reference trajectories can be obtained with optimal control and how these reference trajectories are mapped to the musculoskeletal control with deep reinforcement learning. Starting from the characteristics of human arm movement to obstacle avoidance experiment, our simulation results confirm the capabilities of our optimization and learning framework for a variety of dynamic movement trajectories. In summary, the proposed framework is offering a pipeline to complement the lack of experiments to record human motion-capture data as well as study the activation range of muscles to replicate the specific trajectory of interest. Using the trajectories from optimal control as a reference signal for reinforcement learning implementation has allowed us to acquire optimum and human-like behaviour of the musculoskeletal system which provides a framework to study human movement in-silico experiments. The present framework can also allow studying upper-arm rehabilitation with assistive robots given that one can use healthy subject movement recordings as reference to work on the control architecture of assistive robotics in order to compensate behavioural deficiencies. Hence, the framework opens to possibility of replicating or complementing labour-intensive, time-consuming and costly experiments with human subjects in the field of movement studies and digital twin of rehabilitation.

Project description:ObjectiveRunning-related musculoskeletal injuries (RRMIs), especially stemming from overuse, frequently occur in runners. This study aimed to systematically review the literature and determine the incidence and prevalence proportion of RRMIs by anatomic location and specific pathology.MethodsAn electronic database search with no date beginning restrictions was performed in SPORTDiscus, PubMed, and MEDLINE up to June 2020. Prospective studies were used to find the anatomic location and the incidence proportion of each RRMI, whereas retrospective or cross-sectional studies were used to find the prevalence proportion of each RRMI. A separate analysis for ultramarathon runners was performed.ResultsThe overall injury incidence and prevalence were 40.2% ± 18.8% and 44.6% ± 18.4% (mean ± SD), respectively. The knee, ankle, and lower leg accounted for the highest proportion of injury incidence, whereas the knee, lower leg, and foot/toes had the highest proportion of injury prevalence. Achilles tendinopathy (10.3%), medial tibial stress syndrome (9.4%), patellofemoral pain syndrome (6.3%), plantar fasciitis (6.1%), and ankle sprains (5.8%) accounted for the highest proportion of injury incidence, whereas patellofemoral pain syndrome (16.7%), medial tibial stress syndrome (9.1%), plantar fasciitis (7.9%), iliotibial band syndrome (7.9%), and Achilles tendinopathy (6.6%) had the highest proportion of injury prevalence. The ankle (34.5%), knee (28.1%), and lower leg (12.9%) were the 3 most frequently injured sites among ultramarathoners.ConclusionThe injury incidence proportions by anatomic location between ultramarathoners and non-ultramarathoners were not significantly different (p = 0.798). The pathologies with the highest incidence proportion of injuries were anterior compartment tendinopathy (19.4%), patellofemoral pain syndrome (15.8%), and Achilles tendinopathy (13.7%). The interpretation of epidemiological data in RRMIs is limited due to several methodological issues encountered.

Dataset Information

Efficient trajectory optimization for curved running using a 3D musculoskeletal model with implicit dynamics.

Publications

Efficient trajectory optimization for curved running using a 3D musculoskeletal model with implicit dynamics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets