Thecla Schiphorst

From Motion Capture to Meaningful Movement: LMA as a Design Resource for Digital Technology

Thecla Schiphorst, Karen Bradley & Karen Studd

This chapter addresses the overarching questions of how and why we perceive movement and what draws us to capture, analyze, model, reconstruct and perform that movement through increasingly mediated uses of technology. We frame this inquiry from the perspective of how Laban Movement Analysis (LMA) can contribute to and enrich the design of digital technologies particularly in applications that focus on socio-cultural interaction.

Both LMA and Human Computer Interaction (HCI) have rich historical and technical epistemologies. Each contains specialized language and taxonomies for describing human experience. In our movement-centered research we bridge expert knowledge from LMA focusing on evolving design practices within digital technology, particularly in the domain of movement capture, analysis and meaning.

Human movement is ubiquitous. Howard Gardner identified bodily-kinesthetic intelligence as one of human’s multiple intelligences. What is less well understood is that bodily-kinesthetic ways of knowing are foundational to adaptation, cognition and social evolution. Neuroscientists such as Daniel Wolpert insist that “producing adaptive and complex movement” is a primary goal underlying our brain’s evolution; understanding movement is the precursor to comprehending the brain, sensory perception, memory, intellect and our social consciousness.

Laban Movement Analysis reveals both the detail and the big picture view of a movement event. LMA provides a rich and nuanced language to describe movement. Tremendous implications for how we communicate, conceive, experience, and interpret movement are revealed through the systematic, legible models developed within LMA. Its strength is in its descriptive ability to be translated into computational forms, which can radically benefit digital interaction.

Within existing technology, it is possible to represent human movement in ways that approximate in-vivo human movement interactions. But such mappings are generally limited to gestures and facial expressions, often overlooking the subtle changes in the torso, or the larger shape changes in the entire body, reflecting behaviors of expression and response. We need to unpack how we sense movement both physically and technologically, how to represent the data, and build semantic models that can articulate the wealth of cultural movement experience.

Contemporary applications of motion capture include detailed representations of human movement that support participant/observer motor skill development. This chapter will unpack the ways in which LMA has and is being used, and ways in which future of technological design and expression are evolving. Current research we are aware of or affiliated with include:

• Capturing qualitative aspects of movement (LMA Effort Shape)
• Using EEG technology to trace movement executed and/or imagined and to observe patterns of thought and action
• Mapping components of movement that generate musical, visual or lighting shifts
• Using floor pressure sensors to measure and recreate impact and rhythm
• Recognizing tactile qualities of gestures in soft-circuits embedded within textile surfaces
• Utilizing LMA principles to design creative constraints for generating movement

Digital technologies are at the edge of a deeper understanding of the geography and neurology of human movement, and Laban Movement Analysis is contributing to the shift through its ability to provide models for the future of technological design and expression, providing a roadmap for future research.

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