Medical Engineering & Physics

Temperature rise data by conventional drilling method and vibration-assisted drilling method.

These functions allow estimating Initial Contact events from AnteriorPosterior acceleration signals, based on a Dynamic Time Warping function that fits a triangle wave to the acceleration register. After the initial fit, step correction and optimization strategies are implemented based on methods validated in the literature.

Excel data files relating to measurements of forearm tissue stiffness and forces exerted on pads designed using the pin array tool

A data-fitting program for the modified AVP model as well as the left ventricular pressure and flow measured from a patient.

This code e-CTM.m is part of the paper entitled "Extended Central Tendency Measure and Difference Plot for Heart Rate Variability Analysis" submitted to Medical Engineering & Physics. Developed by Dr. Laurita dos Santos email: lauritas9@gmail.com

The data file includes the MATLAB-based validation tool, exemplary data, and a user guide.

Data for the published paper entitled: "Microarchitecture and morphology of bone tissue over a wide range of BV/TV assessed by micro-computed tomography and three different threshold backgrounds"

This is the data associated with: Prince M, Kenney L, Howard D. A pin-array method for capturing tissue deformation under defined pressure distributions and its application to prosthetic socket design. Med Eng Phys. 2020; 84: 136-143.The Excel file is structured as follows:Tab 1: Raw data for figure 2Tab 2: Time series data used to generate figure 4aTab 3: Time series data used to generate figure 4bTab 4: Pad forces for study 2 (force required to push the pad down to the datum level)

The codes are based on a method for evaluation of the consistency of human movements within the context of physical therapy and rehabilitation. The proposed approach employs an autoencoder neural network to project the high-dimensional motion trajectories into a low-dimensional manifold. Afterward, a Gaussian mixture model is used to derive a parametric probabilistic model of the density of the movements. The resulting probabilistic model is employed for evaluation of the consistency of unseen motion sequences based on the likelihood of the data being drawn from the model.

Link to the data used in: Giorgi, M., Dall'Ara, E. (2018) Variability in strain distribution in the mice tibia loading model: preliminary study using digital volume correlation. Medical Engineering and Physics 2018 The images and data are too large to be uploaded in ORDA, so please contact e.dallara@sheffield.ac.uk or the Project Management Office at: pmo@insigneo.org if you are interested in working on the files. Only an example of a dataset has been uploaded.In the B-03-a folder the preloaded image (Scan1_crop.dcm) has been registered with three different files "Scan2_cop.dcm" that represent:01_Scan2_preload_REGwith_Scan1_load: registration between the second preloaded image and the first loaded image 01_Scan2_preload_REGwith_Scan2: registration between the second preloaded image and the repeated image in the loaded configuration 01_Scan2_preload_REGwith_Repositioning: registration between the second preloaded image and the image of the loaded specimen taken after repositioning In the folder C-03-a-02-Nodes the results from the deformable registration are reported for each registration for Node Spacing of 50. Registrations have been done with ShIRT followed by the voxel detection algorithms as described in the paper. "output_map.txt" reports the coordinates of the nodes of the grid and the computed displacements"results.txt" reports the principal strains in each node The whole dataset is stored in the University of Sheffield file-store at the link:https://web-unidrive.sheffield.ac.uk/shared/multisim2/WP7/Mario_Giorgi/DVC_Tibia_Precision_Study/