The methodology in this study includes a skeletal model in Opensim (*.osim), which is later amputated through Paraview software; for the analysis, it was modelled a transtibial prosthesis in Solidwork® [18] retaining the parameters of a subject case study [19],[20], in this case a prosthesis with a dynamic foot. The model scaled to the anthropometric characteristics of the subject has been fed by the measurements of motion capture the equipment of Technaid®, equipment composed by inertial sensors that act as markers of the model. From the input information the inverse kinematics of OpenSim® was obtained, Fig. 1. This study was approved for Bioethical Committee of Universidad Distrital Francisco José de Caldas, Bogotá, Colombia.
Since only position variations were made for the subject in the socket, for the analysis of the de-alignment by foot effects a neural network was trained, to establish the effects on the gait. Taking into account that whenever a variation in the alignment of the prosthesis was required, it was necessary to modify the * .osim model. In addition, a script in MATLAB® to automatically generate the model was implemented.
2.1 Amputation model in Paraview
Given the characteristics of individual unilateral transtibial amputee, it is essential to perform the model of amputation, tibia and fibula. For this purpose, it is important to emphasize that each part of the body is defined in Opensim® as a * .vpt file. Each bodypart was constructed as a mesh of finite elements, which give the proper geometry; in this case, the cut considered the amputation of the individual under study (13.8 cm), since this occurs in the first third of the knee.
2.2 Position of the markers
For the acquisition of the trajectories of the gait it was used Technaid® system. This method is composed by inertial sensors that can measure the position in angles in the three planes. For this study, the markers of the test equipment were located: chest, thighs, tibia and foot (in the case of the prosthesis, it will be in the equivalent physical part). The sensor that measures the groin is taken as a reference; the sensors are accelerometers and measure the angles in the frontal view, sagittal and coronal, on themselves. Once the data capture session is performed, the data is imported through a routine in the Matlab® program, which converts the angles measured by the equipment into vector coordinates with a canonical basis. Each vector has a different origin, an angle determined by the equipment and a unit value quantity; these predefined values create an animated movement of the two lower extremities, in which the trajectory of the vectors are based on the model of an inverted pendulum.
For the construction of reference, these systems were fixed at the end of each section of the lower limb, however, z=0 it was assumed in the frontal view and x=0 in the sagittal view. To observe the behaviour of the gait in these planes and to realize a three-dimensional view as a vectorial sum, this is made between the two planes.
The Teta θ and Cita angles mentioned in the different origins of the coordinate systems are provided by a ".capa" file (which is provided by the Technaid® equipment). These angles are temporarily stored in Matlab® by creating numeric arrays, whose header name is the part of the body on which the sensor made measurements. The array is decomposed into lists, which have a value in the vector components, at the end each array row becomes a frame describing the movement of a segment of the animation. That is, Matlab® takes 12 vectors of the file ".capa", and each source assigns two values corresponding to θ and angles, in order to carry out the transformation of coordinates and draw a line between the origins according to the calculations made (Fig. 2).
To perform the calculations, a value of each vector is taken at an instant of time (Fig. 2), and Eqs. (1),(2),(3),(4) are solved for the sagittal plane and (5),(6) and (7) for the front plane.
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)
2.3 Simulation of the gait in Matlab®
A virtual representation of the human gait was realized from the interpolation of the articular coordinates, the lines drawn from the calculated coordinates are plotted on the planes. To completely traverse the vectors, a number of iterations is performed, and it is also determined by the number of frames provided by the file ".capa", the time between erasures is determined by (8). Finally, to combine the motion in the 3D (Fig. 3), then a vector sum is applied as shown in Eqs. (9),(10) [18],[19].
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)
2.4 Inverse Kinematics
Kinematics is the study of systems that describes the motion of lines, points and objects without contemplating the forces and moments that produce that motion. kinematics studies trajectories to provide a description of the spatial position of bodies, also motion, velocity, and acceleration, and can represent them in mathematical expressions. In some kinematical analyses, such as inverse kinematics, mass and inertia properties are not needed. The objective of this technique is to facilitate identify the motion of a body to reach a specific location, inverse kinematics equations finds the joint angles required of the model needed to place the body to that location. The experimental kinematics used by the inverse kinematics is based on an experimental marker position (Fig. 4)