Master thesis: Contact estimation For body motion capturing, a popular solution is using inertial measurement units (IMUs). These small sensors provide among others 3D linear acceleration including gravity and 3D rotational velocity. By placing them on every major limb segment and taking advantage of a biomechanical model, intrinsic motion capturing can be performed. A major drawback, however, is the relative nature of these sensors which makes extrinsic tracking impossible in first place. Double integrating acceleration over time results quickly in huge errors. A robust contact estimation can bound these errors by giving information, which point has zero velocity. The goal of this thesis is a robust classification algorithm based on raw sensor data. Suggested task: • Research and evaluate existing approaches to detect zero velocity from inertial measurements. • Develop and implement a classification algorithm based on a sliding window of raw sensor data. • Evaluate your approach against the existing algorithms. Requirements: • Advanced C++ skills • Preferably experience with intertial sensors References: • Skog, Isaac et al., 2010, Zero-Velocity Detection – An Algorithm Evaluation This thesis will be performed in the new research group wearHEALTH (Department of Computer Science, Technical University Kaiserslautern) in cooperation with the Augmented Vision Department (AV) at DFKI. You will be supervised by a postdoc and a Ph.D. Student from wearHEALTH. Please send your application to the below contact! Markus Miezal Technical University Kaiserslautern Department for Computer Science AG wearHEALTH Gottlieb-Daimler-Straße 48 D-67663 Kaiserslautern Phone: +49(0)631 205 75-2644 Email: [email protected] www.wearhealth.org 27.04.2015