Thesis: 3D motion modeling from wearables
Inertial measurement units (IMUs) enable biomechanists and rehabilitation researchers to continuously measure the kinematics of a variety of humans in their natural environment, thereby improving our understanding of human movement pathology. Previous studies have computed IMU-based estimates of kinematics using biomechanical models with high accuracy, but most of these studies used commercially available, and therefore proprietary, models that are costly and usually not completely reproducible. New developments in research around sensor fusion algorithms, as well as methods for sensors to body segment positioning, provide the opportunity to explore new approaches. With the integration of a new working environment called OpenSense into the well-established open source software OpenSim for musculoskeletal modeling, the analysis of IMU data is now supported. However, this support is limited to two commercially available IMUs and Python sample code.
The goal of this project is to develop a workfow for computing three dimensional joint kinematics from IMU sensors using a human biomechanical model, and evaluate it against optical motion capture for different Activities of Daily Living (ADL). We will compare different sensor fusion algorithms and their outputs to compute IMU-based estimates of kinematics in OpenSense and we will perform an error analysis, which takes into account the effects of e.g. missing IMUs that provides information on how the system must be designed.
|Project type||Master thesis|
|Period||Winter term 2021/2022|
|Presence time||Virtual, mostly working from remote, depending on the needs|
|Useful knowledge||3D modeling, programming, biomechanics|
|Work distribution||70% programming and algorithm development, 30% simulation|
|Registration||E-Mail to email@example.com|
Literature recommendations are provided during the meetings. The candidate is further encouraged to research relevant publications on this topic.
Final presentation and final thesis