EFFORTDETECT

Interactive System – Motion Capture + Interactive Machine Learning

Collaboration with Diego Silang Maranan, Thecla Schiphorst, Philippe Pasquier, Pattarawut Subyen, Lyn Bartram (SIAT – SFU)

 

astraldancepractice2While 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 in this project 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.

 

Publication: Diego Silang Maranan, Sarah Fdili Alaoui, Thecla Schiphorst, Philippe Pasquier, Pattarawut Subyen, Lyn Bartram. “Designing For Movement: Evaluating Computational Models using LMA Effort Qualities”, In Proceedings of ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), Toronto, 2014. CHI2014_Marananetal