A preview of the PDF is not available
Recognizing Movement Qualities: Mapping LMA Effort Factors to Visualization of Movement
Abstract and Figures
Our research explores methods for developing new models and computation for utilizing meaning in movement. We present a prototype wearable system titled EffortDetect for extracting and analyzing human movement quality data using a single accelerometer. This system applies Laban Movement Analysis (LMA), a rigorous framework for understanding and analyzing human movement, to recognize different movement qualities from acceleration input. Our machine-learning software is used to classify movement qualities as they are performed in real time. We provide an example of a real-world use of the system and identify questions for discussion in the workshop.
Figures - uploaded by Diego S Maranan
All figure content in this area was uploaded by Diego S Maranan
Content may be subject to copyright.