Classifying Indian Classical Dances By Motion Posture Patterns

Dance is a classic form of human motion which is usually performed as a reaction of expression to music. The Indian classical dances, for instance, require multiple complicated movements that relates to body motion postures and hand gestures with high similarities. Past studies showed interests using various methods to classify dances. The most common method used is the Hidden Markov Models (HMM), apart from using the correlation matrix method and hierarchical cluster analysis. Nevertheless, less effort has been placed in analysing the Indian dance by using the data mining approach. Therefore, the objectives in this work are to (i) distinguish different types of Indian classical dances, (ii) classify the type of dance based on motion posture patterns and (iii) determine the effects of attributes on the classification accuracy. This study involves five types of Indian classical dances (Kathak, Bharatanatyam, Kuchipudi, Manipuri and Odissi) motion postures. The data mining approaches were used to classify the motion posture patterns by type of dances. A total of 15 dance videos were collected from the public available domain for body joints tracking processes using the Kinovea software. Data mining analysis was performed in three stages: data pre�processing, data classification and knowledge discovery using the WEKA software. RandomForest algorithm returned the highest classification accuracy (99.2616%). On attribute configuration, y-coordinates of left wrist (LW(y)) was identified as the most significant attribute to differentiate the Indian classical dance classes

Dance Gesture Recognition Using Space Component And Effort Component Of Laban Movement Analysis

Dance is a collection of gestures that have many meanings. Dance is a culture that is owned by every country whose every movement has beauty or meaning contained in the dance movement. One obstacle in the development of dance is to recognize dance moves. In the process of recognizing dance movements one of them is information technology by recording motion data using the Kinect sensor, where the results of the recording will produce a motion data format with the Biovision Hierarchy (BVH) file format. BVH motion data have position compositions (x, y, z). The results of the existing dance motion record will be extracted features using Laban Movement Analysis (LMA), where the LMA has four main components namely Body, Shape, Space, and Effort. After extracting the features, quantization, normalization, and classification will be performed. Using Hidden Markov Model (HMM). In this study using two LMA components, namely Space and Effort in extracting features in motion recognition patterns. From the results of the test and the resulting accuracy is approaching 99% for dance motion data

3D Information Technologies in Cultural Heritage Preservation and Popularisation

This Special Issue of the journal Applied Sciences presents recent advances and developments in the use of digital 3D technologies to protect and preserve cultural heritage. While most of the articles focus on aspects of 3D scanning, modeling, and presenting in VR of cultural heritage objects from buildings to small artifacts and clothing, part of the issue is devoted to 3D sound utilization in the cultural heritage field