Publikation

Automatic Movement Segmentation of Exoskeleton Data

Lisa Gutzeit, Marc Tabie, Elsa Andrea Kirchner

In: Conference Proceedings of the 3rd International Mobile Brain/Body Imaging Conference. International Mobile Brain/Body Imaging Conference (MoBI-2018) July 11-14 Berlin Germany Seiten 62-63 TU Berlin DepositOnce 7/2018.

Abstrakt

We present a method to automatically segment manipulation movement demonstrated with an exoskeleton into distinct action units. The automatic segmentation of movement plays an important role in applications such as robotic learning from demonstration [4], as well as in braincomputer interfaces when labels for machine learning methods are needed, e.g. for movement prediction [8]. The presented segmentation method is motivated by the hypothesis that human movement is composed of simple building blocks which can be combined to complex behavior [2]. In manipulation movements, these building blocks are characterized by a bell-shaped velocity profile of the hand [5]. We use this to segment human movement trajectories of manipulation tasks into movement building blocks.

Projekte

Weitere Links

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence