Designing a kinect camera-based API to detect qualities of movement

Despite enormous advances in technology, digital interfaces continue to rely on text and image based communication, demonstrating a lack of support for full-body sensory engagement, and downplaying the body’s role in communication and experience. This dependence on visual modes of input and output relies heavily on an individual’s attention, presenting problems for people performing tasks such as walking or driving. There is currently much work being done on gestural movement interaction as an alternative input method with most research addressing functional or task-oriented movement. These approaches, however, neglect the semantic and expressive qualities of movement which constitute an alternative language for interaction. The proposed internship is part of an ongoing investigation of the ways in which embodied meaning is attributed to photographic content through the use of movement-based tagging. This internship will focus on the development of a photo tagging application utilizing a Microsoft© Kinect Camera to capture users’ movements. The intern will focus on the development of the software for this system that will analyze and categorize the characteristics of an individual’s movements based on the Laban Movement Analysis (LMA) framework. The development of a system capable of this type of movement detection has the potential to transform how technologies are used and to promote greater accessibility. It would also benefit the industry partner by providing new competitive capabilities in future software developed by Reality Controls.

Faculty Supervisor:

Thecla Schiphorst


Aaron Levisohn


Reality Controls


Interactive arts and technology


Digital media


Simon Fraser University



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