abdnbiomecheng / techforparalysis Goto Github PK

• Test by adding forces in different locations and orientations

Add Biceps Brachii, and Pronator Teres. The previous elbow model only has Brachialis.

The goal of the optimisation is for the model (with the arm support, if necessary) to successfully complete a series of movement simulations.

The movements are able-bodied movements from our database. The model is one of the virtual patients. Given a set of muscles under sFES, the simulation tries to find the required activation patterns for muscles under voluntary control, and sFES patterns for muscles under stimulation that will produce the desired motion as well as possible. The simulation has the option of adding an arm support, in which case it also optimises the positioning and assistance level of the support. The process is repeated for all desired motions, and all candidate sFES muscle sets, until the optimal set is identified.

• Find set of desired movements
• Change model parameters to match one of the virtual patients
• Set up optimisation cost function and constraints
• Run optimisation

Create C/Matlab version of the model, that can be used for real-time forward dynamic simulations or optimal control.

• Check status of all software required (Autolev?!, C compilers ...)
• Find previous attempt to do this for the whole arm
• Simplify according to #1
• Add external forces to all segments
• Run code

A script is written - debug it and add IK analysis.

Add Martin to team, and move Rosti to alumni.

We will use the simulation data for the optimal sFES muscle set to optimise the controller for the stimulation.

We will train an artificial neural network (ANN) to predict the sFES patterns from voluntary muscle activations that we can record using surface electromyography. If injury level is such that certain motions can be done independently (e.g. paralysis at the elbow and distally, but not at the shoulder), recorded kinematics will also be used as inputs to the ANN.

• Separate voluntary muscle activations from sFES patterns
• Create training, validating and testing data sets
• Train ANN

Remove deprecated Schutte muscle model and replace with Mallard?

Requires C coding to interface with the library 😕

Since we are focusing on grasp, it should be a simplified hand model with limited degrees of freedom.

• Find the latest version of the whole-arm model
• Look into the simple hand used in the Murray model
• Decide on which degrees of freedom and muscles to include in the forearm and hand
• Modify whole-arm model

We want to test our methodology and FES controller in the presence of noise, as well as under ideal conditions. The noise could be errors in clinical/biomechanical measurements that mean our model patient is different from the actual patient, or sensor errors so that the arm position is not fed back to the FES controller correctly.

• Add uncertainty to the system optimisation and retrain the FES controller
• Add variation to the model parameters
• Add noise to the feedback signals

Nice pics to inspire people 😄

• Successfully run all examples that come with moco
• Set up moco to run with simple elbow model
• Run optimisation 1: prescribe elbow angle
• Run optimisation 2: track elbow angle in the cost function
• Run optimisation 3: specify only start and end angle and the duration of movement

This is for runopt_elbow.m (lines 36 & 37)

Also known as Rehamove3.

Create three categories (theoretical patients) with the most common characteristics due to Cervical level injury.

We want it to be easy to set up the optimisation based on system constraints, desired movements, etc.

• Decide on which optimisation settings users will need to modify
• Decide on the best way for users to specify settings
• Build interface for setting up optimisation

Add short bio and photo for each co-investigator on the "Meet the team" page