A mathematical model was developed in MATLAB and was used to inform the design of a 3D printed percussion device, that could be programmed to percuss a silicon phantom.
The force was measured using a load cell and compared to the force profile of a manual medical percussion.
A contact microphone was used to collect the acoustic response from several hundred percussion events. The audio samples were trimmed and de-noised with Matlab and Python scripts.
I assigned reference frames to each link of the robots arm using the Denavit-Hartenberg convention.
Scalograms were generated for each percussion event and these were used to train and validate a convolutional neural network. The CNN was able to classify the presence of a nodule accurately, and saliency maps were used to check which time-variant frequencies informed the neural networks classification.
For more details about the project's code as well as our papers and posters, check out the project's github here.