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

EEG Based Gesture Mimicking by An Artificial Limb Using Cascade-Correlation Learning Architecture

Patients with prosthesis defects find it is very difficult to perform day-to-day basic tasks which involve employment of their limbs. This motivates us to develop a system where an artificial limb is employed to mimic the arm gestures of the patients for assisting them. Towards developing this system, we have taken the help from the electroencephalography (EEG) signals acquired from the brain of the patients to build a bypass network (BPN) to direct the artificial limb. Since difficulties are already present in the arm movements of the patients (here subjects), thus only gestures of those subjects are not sufficient to build the proposed system. This research finds tremendous applications in rehabilitative aid for the disable persons. To concretize our goal we have developed an experimental setup, where the target subject (for training phase healthy subjects are taken into account) is asked to catch a ball while his/her brain (occipital, parietal and motor cortex) signals using EEG acquisition device and body gestures using Kinect sensor are simultaneously acquired. These data are mapped using four cascade-correlation learning architecture (CCLA) to train artificial limb (we have used Jaco robot arm) to move accordingly. Utilizing the mapping results obtained from these four CCLAs, a BPN is developed. When a rehabilitative patient is unable to catch the ball, then in that scenario, the artificial limb is helpful for assisting the patient to catch the ball with a high accuracy of 85.65%. The proposed system can be implemented not only for ball catching experiment but also in several applications where an artificial limb needs to perform a locomotive task based on EEG and body gesture