Hello Kyle,
To develop a transfer function to translate the blast data from animal models to the human dataset, you can use the tools available in the Curve Fitting Toolbox to perform the optimization and fitting. Given the nature of your question, I can demonstrate a general method to set up a second-order model and fit your data to such a model, rather than providing you with the exact optimizations.
Please follow the code snippet below to understand how to set up a second-degree model and fit your data to it:
% Step 1: Load the Data
% Load the data into ‘lunginset’ using the ‘Data Import Tool’ by double clicking on the file in the ‘Current Folder’ window
data = lunginset;
% Extract the relevant columns
ferret_incident = data.FerretIncident;
ferret_lung = data.FerretLungPressure;
human_incident = data.PMHSIncidentPressure;
human_lung = data.PMHSLungPressure;
nhp_incident = data.NHPIncident;
nhp_lung = data.NHPLungPressure;
% Step 2: Normalize the Lung Pressures using Incident Pressures
normalized_ferret_lung = ferret_lung ./ ferret_incident;
normalized_human_lung = human_lung ./ human_incident;
normalized_nhp_lung = nhp_lung ./ nhp_incident;
% Step 3: Define the Model (Second-order polynomial)
model = @(b, x) b(1) * x.^2 + b(2) * x + b(3);
% Step 4: Fit the Model to Ferret Data
opts = fitoptions('Method', 'NonlinearLeastSquares', 'StartPoint', [1, 1, 1]);
fitType = fittype('b1*x^2 + b2*x + b3', 'independent', 'x', 'coefficients', {'b1', 'b2', 'b3'});
[fitresult_ferret, gof_ferret] = fit(normalized_ferret_lung, normalized_human_lung, fitType, opts);
% Fit the Model to NHP Data
[fitresult_nhp, gof_nhp] = fit(normalized_nhp_lung, normalized_human_lung, fitType, opts);
% Step 5: Optimize the Parameters (if necessary)
% You can use additional optimization techniques if needed
% Step 6: Validate the Model
% Compare the fitted results with the human data
predicted_human_from_ferret = model(coeffvalues(fitresult_ferret), normalized_ferret_lung);
predicted_human_from_nhp = model(coeffvalues(fitresult_nhp), normalized_nhp_lung);
% Plot the results for validation
figure;
subplot(2,1,1);
plot(normalized_ferret_lung, normalized_human_lung, 'o', normalized_ferret_lung, predicted_human_from_ferret, '-');
title('Ferret to Human Data');
legend('Actual Human Data', 'Predicted Human Data');
xlabel('Normalized Ferret Lung Pressure');
ylabel('Normalized Human Lung Pressure');
subplot(2,1,2);
plot(normalized_nhp_lung, normalized_human_lung, 'o', normalized_nhp_lung, predicted_human_from_nhp, 'o');
title('NHP to Human Data');
legend('Actual Human Data', 'Predicted Human Data');
xlabel('Normalized NHP Lung Pressure');
ylabel('Normalized Human Lung Pressure');
On fitting the data on the first 30 rows, thereby avoiding missing data, I obtained the following results.
This should give you a starting point and a template to perform your task with the tools available in the toolboxes that you can access.
For further understanding, kindly refer to the following MathWorks documentation:
- Refer to the ‘Examples’ section to understand how to fit curves or surfaces to data: MathWorks Documentation.
I hope you find the above explanation and suggestions useful!
