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EP/I033602/1 - A Large-Scale Predictive Musculoskeletal Model to Simulate Human Walking

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Dr L Ren EP/I033602/1 - A Large-Scale Predictive Musculoskeletal Model to Simulate Human Walking

Principal Investigator - Mechanical Aerospace and Civil Eng, The University of Manchester

Scheme

First Grant Scheme

Research Areas

Assistive Technology, Rehabilitation and Musculoskeletal Biomechanics Assistive Technology, Rehabilitation and Musculoskeletal Biomechanics

Start Date

03/2012

End Date

01/2014

Value

£99,995

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Grant Description

Summary and Description of the grant

Walking is our most fundamental and widespread mode of transportation and is recommended for a healthy lifestyle. We walk automatically without the need for conscious attention. However, this seemingly simple task requires very complex control between our body segments, joints and muscles, working together to provide highly adaptable and energy efficient forward movement. What are the basic mechanisms underlying human walking? What are the fundamental control strategies governing human walking. There are still many unsolved questions toward the fundamental understanding of human walking. Most of experimental studies have been descriptive in nature as they are based on measurements, and by its very nature tell us what happens but not why it happens. While mathematical models, which can predict human walking without the need of measurement data, has the capacity to tell us why it happens because the reasons for particular model behaviours can be examined.The aim of this study is to develop a novel computer model to predict human walking using an advanced computational approach, which has great potential for the simulation of large-scale system and has not yet been fully explored. Differing from previous studies, all the body motions and forces exerted on the body during walking will be predicted by only defining walking speed and distance travelled in a stride without the need of measurement data. We will also conduct walking measurements on the same person used for the model construction to thoroughly validate the prediction results of the model. This will go far beyond previous attempts at human walking prediction, creating a highly novel and very computationally efficient model to predict human walking with minimal measurement inputs. A thoroughly validated predictive model would help to answer very fundamental questions underlying human walking and lead to deeper insight into the mechanisms underlying human walking. The predictive walking model would have many important practical applications. For example, for people with movement disorders, e.g. cerebral palsy, the predictive model could be used to fully understand how the change of their body structures affects their walking pattern and therefore will allow clinicians to develop better treatment plans for their patients. Current techniques for assessing artificial limb (prosthetic) design for people those lost their legs are largely based on experimental observations and subjective user feedback. The predictive model could be used as part of a novel computer aided design process for predicting the effects of new prosthetic components on the human walking performance. This would be a significant step toward a more systematic and objective design process, which would help reduce the reliance on the costly and time-consuming process using physical models and human testing. Similarly, this technique can also be used in many manufacturing and military companies who need novel computer aided techniques based on advanced computer model of human walking to reduce expensive physical testing and human evaluation of their products, e.g. sports products, personal load carriage system etc.Our novel computational model will provide a powerful analysis framework for understanding of the fundamental mechanisms underlying human walking and also provide the basis for future computer aided design technique. With its strong links to clinical applications it will also build a new foundation to broadly benefit health and welfare.

Structured Data / Microdata


Grant Event Details:
Name: A Large-Scale Predictive Musculoskeletal Model to Simulate Human Walking - EP/I033602/1
Start Date: 2012-03-26T00:00:00+00:00
End Date: 2014-01-05T00:00:00+00:00

Organization: The University of Manchester

Description: Walking is our most fundamental and widespread mode of transportation and is recommended for a healthy lifestyle. We walk automatically without the need for conscious attention. However, this seemingly simple task requires very complex control between our ...