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Master’s in Movement Neuromechanics and Musculoskeletal Modelling Engineering

Online master's programme of 1,900 hours in movement neuromechanics and musculoskeletal modelling engineering: 12 modules covering motor control neurophysiology, musculotendon mechanics, joint kinematics, neuromechanical data acquisition, model building, inverse dynamics, forward dynamic simulation, neural control modelling, clinical applications, coupled finite elements and machine learning, with the ISB recommendations, ASME V&V 40 and OpenSim and Moco, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Understand the neural and mechanical mechanisms of movement control.

02

Model musculotendon dynamics with Hill-type models.

03

Build and scale subject-specific musculoskeletal models.

04

Estimate muscle and joint forces through inverse dynamics and optimisation.

05

Develop predictive simulations using optimal control.

06

Validate computational models following credibility criteria.

07

Apply neuromechanical modelling to clinical and sports decisions.

Profile

Who it's for

  • Neuromechanics researchers.
  • Musculoskeletal modelling engineers.
  • Clinical gait analysts at hospital gait laboratories.
  • Biomedical simulation engineers.
  • Scientific software developers.
  • Orthopaedic R&D engineers.
  • Engineering or science graduates looking to work in hospitals and gait laboratories.
Standards & tools

What you'll work with

  • ISB recommendations on joint coordinate systems (International Society of Biomechanics conventions)
  • ASME V&V 40 (assessing credibility of computational models for medical devices)
  • SENIAM recommendations for surface EMG (sensor placement and signal processing)
  • WMA Declaration of Helsinki (ethical principles for research involving human subjects)
  • Regulation (EU) 2016/679 (GDPR) (protection of personal data)
  • FDA guidance: Reporting of Computational Modeling Studies in Medical Device Submissions (reporting of computational models for regulatory submissions)
  • OpenSim and Moco
  • MATLAB and Simulink
  • FEBio
  • AnyBody Modeling System
  • Python with NumPy and PyTorch
Syllabus

12 modules · 1900 h

  1. 1.1The motor unit and Henneman's size principle
  2. 1.2Corticospinal pathways and descending control
  3. 1.3Spinal reflexes and feedback loops
  4. 1.4Proprioception, muscle spindles and Golgi tendon organs
  5. 1.5The cerebellum and motor learning
  6. 1.6Muscle synergies and redundancy
  7. 1.7Optimal control theories
  8. 1.8Motor variability and noise
  9. 1.9Sensorimotor integration and internal models
  10. 1.10Case study: analysis of a stretch reflex
  1. 2.1The three-element Hill model
  2. 2.2Activation and excitation dynamics
  3. 2.3Muscle architecture and pennation angle
  4. 2.4Tendon elasticity and energy storage
  5. 2.5Force–length and force–velocity curves
  6. 2.6Huxley models and cross-bridges
  7. 2.7Zajac parameters and normalisation
  8. 2.8In vivo parameter identification
  9. 2.9Fatigue and potentiation in the muscle model
  10. 2.10Practical: simulating a contraction in MATLAB
  1. 3.1Helical axes and centres of rotation
  2. 3.2Coordinate systems recommended by the ISB
  3. 3.3Euler and Cardan angles
  4. 3.4Quaternions and representation of rotations
  5. 3.5Knee and shoulder joint models
  6. 3.6Muscle moment arms
  7. 3.7Open and closed kinematic chains
  8. 3.8Functional estimation of joint centres
  9. 3.9Measurement errors due to skin artefact
  10. 3.10Lab: joint centres from motion capture
  1. 4.1Optoelectronic motion capture
  2. 4.2Force platforms and synchronisation
  3. 4.3Surface and high-density EMG
  4. 4.4Fine-wire intramuscular EMG
  5. 4.5Ultrasound for muscle architecture
  6. 4.6Magnetic resonance imaging for subject-specific models
  7. 4.7Dynamometry and calibration protocols
  8. 4.8Data management in C3D format
  9. 4.9Time synchronisation between measurement systems
  10. 4.10Lab: multimodal gait and EMG session
  1. 5.1Gait2392 and Rajagopal generic models
  2. 5.2Anatomical scaling to the subject
  3. 5.3Subject-specific models from imaging
  4. 5.4Definition of muscle paths and via points
  5. 5.5Muscle wrapping surfaces
  6. 5.6Joint constraints and ligaments
  7. 5.7Modelling the foot and the spine
  8. 5.8Sensitivity to model parameters
  9. 5.9Personalised segment mass and inertia
  10. 5.10Practical: scaling a model in OpenSim
  1. 6.1Least-squares inverse kinematics
  2. 6.2Inverse dynamics and net joint moments
  3. 6.3Static optimisation and cost criteria
  4. 6.4Computed Muscle Control
  5. 6.5The muscle redundancy problem
  6. 6.6EMG-driven models
  7. 6.7Estimated joint contact forces
  8. 6.8Comparison with instrumented implants
  9. 6.9Induced acceleration analysis of muscle contributions
  10. 6.10Practical: static optimisation of a jump
  1. 7.1Multibody equations of motion
  2. 7.2Numerical integration and system stiffness
  3. 7.3Optimal control by direct collocation
  4. 7.4Moco and predictive simulations
  5. 7.5Foot–ground contact models
  6. 7.6Predictive gait generation
  7. 7.7Reflex and neuromuscular controllers
  8. 7.8Simulating the effect of surgery
  9. 7.9Simulation of assistive devices and exoskeletons
  10. 7.10Lab: predictive simulation with Moco
  1. 8.1Central pattern generators
  2. 8.2Feedback control models
  3. 8.3Integration of reflex models
  4. 8.4Muscle fatigue models
  5. 8.5Plasticity and motor adaptation
  6. 8.6Postural control and balance
  7. 8.7Inverted pendulum models
  8. 8.8System identification in motor control
  9. 8.9Functional electrical stimulation and its modelling
  10. 8.10Practical: postural control model in Simulink
  1. 9.1Gait in cerebral palsy
  2. 9.2Surgical planning of tendon transfers
  3. 9.3Knee osteoarthritis and joint loading
  4. 9.4Stroke and compensation patterns
  5. 9.5Model-based orthosis design
  6. 9.6Biomechanics of the lumbar spine
  7. 9.7Assessment of joint prostheses
  8. 9.8Communicating results to the clinical team
  9. 9.9Simulation-guided gait rehabilitation
  10. 9.10Case study: simulation of hamstring surgery
  1. 10.1From the multibody model to the FE model
  2. 10.2Meshing bones from CT
  3. 10.3Material properties of tissues
  4. 10.4Stress analysis in bone and cartilage
  5. 10.5Coupling with muscle loads
  6. 10.6Computational bone remodelling
  7. 10.7Verification and validation under ASME V&V 40
  8. 10.8Model credibility for medical devices
  9. 10.9Knee models with articular contact
  10. 10.10Lab: femur stresses with FEBio
  1. 11.1Neural networks for force estimation
  2. 11.2Surrogate models for costly simulations
  3. 11.3Kinematics estimation from IMUs
  4. 11.4Reinforcement learning for motor control
  5. 11.5Patient digital twins
  6. 11.6Uncertainty quantification
  7. 11.7Open science and SimTK repositories
  8. 11.8Ethics and patient data
  9. 11.9Real-time estimation for biofeedback
  10. 11.10Practical: neural network for joint moments
  1. 12.1Formulation of the neuromechanical problem
  2. 12.2Review of existing models
  3. 12.3Acquisition or selection of experimental data
  4. 12.4Development of the subject-specific model
  5. 12.5Simulation and sensitivity analysis
  6. 12.6Validation against measurements
  7. 12.7Clinical or sports interpretation
  8. 12.8Publication of code and data
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: you progress at your own pace within the intake, so it is compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is OpenSim and Moco, MATLAB and Simulink, FEBio, AnyBody Modeling System and Python with NumPy and PyTorch, in free, academic or evaluation versions. Practical work is based on real procedures for multimodal capture sessions with EMG and force platforms, predictive gait simulations and model validation against published instrumented implant data. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISB recommendations on joint coordinate systems, ASME V&V 40, SENIAM recommendations for surface EMG, WMA Declaration of Helsinki, Regulation (EU) 2016/679 (GDPR), FDA guidance: Reporting of Computational Modeling Studies in Medical Device Submissions.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of the ISB recommendations, ASME V&V 40 and the SENIAM recommendations applied to real cases, not as a theoretical annex.
Industry software:OpenSim and Moco, MATLAB and Simulink, FEBio, AnyBody Modeling System and Python with NumPy and PyTorch, in free, academic or evaluation versions.
Master's Thesis with defence:A complete neuromechanical modelling study, from problem formulation and subject-specific model development to simulation, validation against measurements and open publication of code and data, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Problem formulation
Formulate the neuromechanical problem and review existing models.
Experimental data
Acquire or select multimodal experimental data with motion capture, force platforms and EMG.
Subject-specific model
Develop and scale the subject-specific musculoskeletal model in OpenSim.
Simulation and validation
Run simulations with sensitivity analysis and validate them against measurements following ASME V&V 40.
Master's Thesis
Present the clinical or sports interpretation with published code and data, defended before a panel.
Career opportunities

Where this takes you

Neuromechanics researcherMusculoskeletal modelling engineerClinical gait analystBiomedical simulation engineerScientific software developerOrthopaedic R&D engineer
Admissions & scholarships

Requirements

  • A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • An internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with OpenSim and Moco, MATLAB and Simulink, FEBio, AnyBody Modeling System and Python with NumPy and PyTorch, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme's software, and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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