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)

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.