Supplementary material to the paper "Embedding anatomical characteristics in 3D models of lower-limb sockets through statistical shape modelling", accepted at the VISAPP 2021 conference. Check the abstract here.
This repository contains examples, in pointcloud format, of transtibial and transfemoral sockets generated using Statistical Shape Models.
These artificial shapes were a part of the work described in the article "Modelling lower limb sockets through Statistical Shape analysis", up for consideration in BIBM 2020 conference.
They were built from two Statistical Shape Models (one pertaining the transtibial, and another the transfemoral sockets), trained on 30 transtibial sockets and 21 transfemoral sockets which were acquired through a laser-based 3D inner socket shape scanner (INSIGHT™ Scanner from Adapttech). Before the model generation, the sockets from the left leg were mirrored, thus all provided examples will be right leg sockets. The new shapes were generated according to the formula:
Where F is the new shape, M is the number of principal components considered, PC is the principal component and b is a weight factor. The weight factors were randomly chosen from plausible ranges, as defined by orthoprosthetists.
The artificially generated point clouds of transtibial sockets had 4 731 points, whereas those of transfemoral sockets had 11 487 pointswere. Those were subjected to a mesh generation through Delaunay triangulation, and its points undersampled to a total of 2048 points.
- Transtibial: 100 .npy files
- Transfemoral: 100 .npy files
To load and visualize one of the generated examples, simply run:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
pcl = np.load("TF/sockettf_008070.npy")
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(pcl[:, 0],
pcl[:, 1],
pcl[:, 2])
plt.show()The shape of this array is (n,3) where n is the number of points.