Generation of subject-specific, dynamic, multi-segment ankle and foot models to improve the design of foot and ankle foot orthoses

Generation of subject-specific, dynamic, multi-segment ankle and foot models to improve the design of foot and ankle foot orthoses - A-FOOTPRINT

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

NL-OMON

Registry ID

NL-OMON38031

Enrollment

Registered

2010-12-15

Start date

2011-08-01

Completion date

Unknown

Last updated

2024-05-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

CVA general foot problems

Interventions

None listed

Eligibility

Age

18 Years to 99 Years

Inclusion criteria

Inclusion criteria: Group healthy feet: • Physically able to perform testing (able to walk at least 20 meters barefoot and unaided) • 38

Exclusion criteria

Exclusion criteria: Group healthy feet • Footproblems, requiring medical treatment • Lower extremity problems • Diabetes Mellitus • Rheumatoid Arthritis • Pregnant and lactating women Group pathological feet • Lower extremity problems, causing gait variation • Pregnant and lactating women

Design outcomes

Primary

Measure	Time frame
This study is a feasibility/pilot study. The primary aim of the study is to collect the imaging and motion analysis data that is required to develop and validate accurate computational models of the foot and ankle, and incorporate these into high fidelity dynamic simulations. As such, the outcome measure could be defined as how close to "real life" these simulations are shown to be. This will be achieved by validating the model against plantar pressure data and surface EMG data taken during the gait analysis.	—

Measure

Time frame

This study is a feasibility/pilot study. The primary aim of the study is to collect the imaging and motion analysis data that is required to develop and validate accurate computational models of the foot and ankle, and incorporate these into high fidelity dynamic simulations. As such, the outcome measure could be defined as how close to "real life" these simulations are shown to be. This will be achieved by validating the model against plantar pressure data and surface EMG data taken during the gait analysis.

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Secondary

Measure	Time frame
nvt	—

Countries

Netherlands

Outcome results