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Designing For Movement: Evaluating Computational Models using LMA Effort Qualities

Abstract : While single-accelerometers are a common consumer embedded sensors, their use in representing movement data as an intelligent resource remains scarce. Accelerometers have been used in movement recognition systems, but rarely to assess expressive qualities of movement. We present a prototype of wearable system for the real-time detection and classification of movement quality using acceleration data. The system applies Laban Movement Analysis (LMA) to recognize Laban Effort qualities from acceleration input using a Machine Learning software that generates classifications in real time. Existing LMA-recognition systems rely on motion capture data and video data, and can only be deployed in controlled settings. Our single- accelerometer system is portable and can be used under a wide range of environmental conditions. We evaluate the performance of the system, present two applications using the system in the digital arts and discuss future directions.
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https://hal.inria.fr/hal-01663093
Contributor : Sarah Fdili Alaoui <>
Submitted on : Tuesday, January 9, 2018 - 7:01:50 PM
Last modification on : Wednesday, January 10, 2018 - 9:00:30 AM

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Diego Maranan, Sarah Alaoui, Thecla Schiphorst, Philippe Pasquier, Pattarawut Subyen, et al.. Designing For Movement: Evaluating Computational Models using LMA Effort Qualities. ACM Conference on Human Factors in Computing Systems (CHI), Apr 2014, Toronto, Canada. ⟨10.1145/2556288.2557251⟩. ⟨hal-01663093⟩

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