motion capture

Although extensively utilized to estimate bone kinematics, skin-marker motion capture is plagued by errors arising from soft-tissue artefact (STA). The error caused by STA is substantial and “puts at risk the validity of a significant body of research in the basic, clinical, and applied sciences”. This project quantifies and visualizes STA for the humerus and scapula in 20 healthy subjects. The generated dataset and visualizations will serve as a guide for designing and validating STA suppression algorithms.

Unlike a traditional socket prosthesis, an osseointegrated (OI) prosthesis attaches directly to the bone of the residual limb. OI prostheses provide upper-extremity amputees increased range of motion, more natural movement patterns, and enhanced proprioception. However, the direct skeletal attachment of the prosthesis elevates the risk of bone fracture. To minimize the risk of fracture, it's important to mechanically characterize the bone-prosthesis interface under the same conditions that it would experience in vivo. In this project, I robotically replicate the motion of the humerus as recorded via motion capture while subjects performed activities typical of an active amputee. The robotically replicated motions will be utilized in future investigations to mechanically characterize the bone-prosthesis interface of an OI prosthesis.

In my first Ph.D. project, I robotically replicated the motion of the humerus as recorded via motion capture while subjects performed activities typical of an active amputee. The first task of this project was to program the position and orientation of the humerus onto the robot. In this post, I describe my method for accomplishing this task. To me, this is an interesting topic because it uses the same concepts as my previous post on establishing the position and orientation of a rigid body; but, the pen and paper are replaced by a robot and motion-tracking system.

The Singular Value Decomposition (SVD) is an incredibly useful tool with a staggering number of applications in seemingly unrelated fields. In this post I want to write about how the SVD is utilized to determine the orientation of a body segment from the skin markers attached to it. It's an interesting application of the SVD because it has straightforward geometrical interpretation.

Before building more complex logic into my codebase, I wanted to compare the performance of different methods of computing common biomechanics linear algebra operations in Numpy. As is common in biomechanics and robotics, I use a 4x4 homogeneous matrix to represent a coordinate system or pose, and correspondingly 3D vectors are upgraded to homogeneous coordinates. I knew that numpy.einsum could accommodate all linear algebra operations I am interested in performing, but I was curious to compare its performance against numpy.matmul for operations that could be performed just by matrix multiplication. Thanks to Numpy's broadcasting algorithm a considerable number of operation can be performed using numpy.matmul.

In Container Emulation in Python I showed how easy it is to emulate container behavior in Python in order to enable concise data access. In this post I want to talk about how this can be accomplished lazily.

The Python data model makes it easy to implement classes that allow concise data access. For example, when analyzing skin marker motion capture data, I frequently access data for a particular marker (identified by its name). Below I demonstrate how to access marker data from a trial (say arm raising and lowering) using an instance method, and via container emulation.