Random Average Distribution aided SVD feature selection and interpretation for touch less robotic control by furtive gestures

• Published in: International Conference on Current Trends in Information Technology (Print) pp. 249 - 253
• Main Authors: Subramanian, Sadhasivam, Izarukku, Zubeir, Rajendran, Kalvina, Samraj, Andrews
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2013
• Subjects: Abstracts; Acceleration; Data Glove; Feature extraction; gesture computing; Matrix decomposition; Random Average Distribution; Robot sensing systems; Singular Value Decomposition; Three-dimensional displays; Wearable computing
• ISSN: 2377-5327
• DOI: 10.1109/CTIT.2013.6749512

The implicit robotic communication is highly appreciated in the fields of security and defense. The reason for such communication is to conceal the commands or its source to others to gain the whole benefits of the hidden robotic assistance to make it available in the case of emergency and critical situations. To interpret and communicate most distinctive orders to the robotic system, a well defined and distinguishable gesture paradigm is necessary. Moreover the conversion of such paradigms should also be precise and augmentative. The Random Average Distribution helps to reduce the complexity of the SVD features and augment the clear classification. The signals acquired from a pair of different gestures from five subjects with ten trials, were subjected to the proposed feature extraction method and were classified. The results obtained were found better than the results obtained by the direct SVD and reduced in complexity.