Computational methods in biomedical engineering design

Learning Outcomes

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Prepare technical reports
• Extract, interpret and present data
• Apply analytical skills to practical problems
• Explain and defend modelling decisions
• Develop advanced adaptive modelling techniques to simulate time dependent processes
• Use standard engineering software to assess failure

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• Suitable methods for developing computational models of the musculoskeletal system
• Customer and end-user needs incl. the importance of aesthetics in presenting dedicated simulation tools
• Techniques for computational modelling of common failure modes, including: bone remodelling; tissue differentiation; damage accumulation and wear
• Selection of appropriate methods to consider both technical uncertainty but also assess the influence of patient and surgical variability
• Implementation design of experiments and probabilistic techniques into computer simulations

Full CEng Programme Level Learning Outcomes

Having successfully completed this module you will be able to:

• As part of the individual assignment students will select and critically evaluate technical literature and other sources of information to solve the specific complex biomedical engineering problems they address.
• As part of the individual assignment students will analyse complex problems to reach substantiated conclusions. This will involve evaluating a variety of biomedical engineering data which can include biomedical imaging, 3D surface and volume model, but also motion capture and other experimental data using first principles of mathematics, statistics, natural science and engineering principles, and using your engineering judgment to work with information and data sets that may be uncertain or incomplete, discussing the limitations of the techniques employed.
• As a central task in the individual assignment students will select and apply appropriate computational and analytical techniques ranging from, e.g. manual to automated image segmentation, various model fitting and data approximation techniques, to analytical and iterative numerical methods to model complex biomedical engineering problems related to optimisation and performance evaluation of methods and devices, discussing the limitations of the techniques employed.
• As part of the individual assignment students will apply a comprehensive knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems in biomedical engineering. Much of the knowledge will be at the forefront of biomedical engineering and informed by a critical awareness of new developments in numerical modelling and analysis methods relevant to the development of technology, devices, and procedures for the prevention, diagnosis, treatment and rehabilitation of ill-health.

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Apply appropriate modelling strategies to assess the performance of biomedical devices and conceive designs that promote sustainable development
• Assess the strengths and limitations of computational models for assessing the performance of biomedical devices
• Identify suitable sources of data to both drive and verify and asses the quality of computational simulations

Partial CEng Programme Level Learning Outcomes

Having successfully completed this module you will be able to:

• In the written reports for their individual assignments students summarise the background of their work, the methods, findings and provide a critical discussion you will communicate effectively on the complex biomedical engineering matters in a manner appropriate for technical and non-technical audiences.

All thematic units include a combination of lectures (L) and hands-on labs (P)

Unit 1: Introduction to techniques for image-based computer model generation and essential analyses.

Unit 2: Computational musculoskeletal modelling techniques: Introduction to back ground an theory; GaitLab: practical introduction to motion capture & assessment of muscle function.

Unit 3: Advanced musculoskeletal modelling techniques: from theory and implementation to practical problem solving using musculoskeletal modelling techniques in OpenSim.

Unit 4: Computational models for the simulation of time-dependent behaviour: from understanding polyethylene wear behaviour to bone adaptation & fracture healing.

Unit 5: Introduction to probabilistic modelling techniques to account for uncertainty and statistical modelling approaches

Unit 6: Review lecture.

Teaching methods include:

• Lectures including the presentation of case studies with comprehensive notes and tutorials
• Supervised practical lab sessions including:
• - computer lab sessions to practice the use of a range of computational modelling techniques
• - a motion capture lab

Learning methods include:

• Individual reading to enhance the breath of understanding
• Individual work on tutorial problems
• Problem-based learning in small groups during lab sessions
• Individual work on assignments

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.