Dance Gesture Recognition using Laban Movement Analysis with J48 Classification

This study describes the introduction of classical dance movements using the Laban Movement Analysis (LMA) method which consists of 3 main components, namely Body, Space, and Shape. How to carry out the classical motion recognition process using Kinect which is then read by the screen using the Brekel Kinect and produces dance motion pictures in different formats (. * BVH). After that, it is calculated using the LMA method by obtaining the results obtained in the form of numerical data from each joint from the direction of the axis (xyz), then classification is carried out using the J48 classification method provided at WEKA tools after 50 training data is carried out. 96% truth is recognized, because it guarantees those who meet the requirements, 12 data tests are carried out apart from training data, which can be 92% accurate on average, so it is very possible that this method can be used in dance preparation, especially in classical dance

Choreographic and Somatic Approaches for the Development of Expressive Robotic Systems

As robotic systems are moved out of factory work cells into human-facing environments questions of choreography become central to their design, placement, and application. With a human viewer or counterpart present, a system will automatically be interpreted within context, style of movement, and form factor by human beings as animate elements of their environment. The interpretation by this human counterpart is critical to the success of the system's integration: knobs on the system need to make sense to a human counterpart; an artificial agent should have a way of notifying a human counterpart of a change in system state, possibly through motion profiles; and the motion of a human counterpart may have important contextual clues for task completion. Thus, professional choreographers, dance practitioners, and movement analysts are critical to research in robotics. They have design methods for movement that align with human audience perception, can identify simplified features of movement for human-robot interaction goals, and have detailed knowledge of the capacity of human movement. This article provides approaches employed by one research lab, specific impacts on technical and artistic projects within, and principles that may guide future such work. The background section reports on choreography, somatic perspectives, improvisation, the Laban/Bartenieff Movement System, and robotics. From this context methods including embodied exercises, writing prompts, and community building activities have been developed to facilitate interdisciplinary research. The results of this work is presented as an overview of a smattering of projects in areas like high-level motion planning, software development for rapid prototyping of movement, artistic output, and user studies that help understand how people interpret movement. Finally, guiding principles for other groups to adopt are posited.Comment: Under review at MDPI Arts Special Issue "The Machine as Artist (for the 21st Century)" http://www.mdpi.com/journal/arts/special_issues/Machine_Artis

Observation scheme for interaction with embodied intelligent agents based on Laban notation

Designing more intuitive interaction with robots and other intelligent agents relies on knowledge gathered through measurement methods that offer a limited perspective, such as questionnaires and naive observers. To broaden the toolset for designers of interactive robots, we developed an observation scheme based on the framework of Laban/Bartenieff Movement Analysis. Instead of limiting the evaluation to certified movement analysts, we have set and translated parameters that can be evaluated by an uncertified observer. The proposed observation scheme aims to provide more objective measures for the unconscious interaction patterns so designers can use it alongside, or instead of self-report, physiological or other measurements. The development was carried out in the context of a study about participants' interactions with an expressive walk-in closet that was able to take on a more dominant or submissive attitude by using dynamic lighting behaviors. Two certified movement analysts (CMA's) determined and translated the main categories of the Laban framework to measurable qualities for the particular experimental setting. The outcome was validated by measuring the inter-rater agreement and the evaluation of 3 CMA's, who evaluated the videos from the experiment. The observation scheme is not explicitly validated with non-certified LMA observers yet, however, the set-up is such that the observer does not require specific prior knowledge on Laban/Bartenieff Movement Analysis