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

Content Provider	IEEE Xplore Digital Library
Author	Subramanian, S. Izarukku, Z. Rajendran, K. Samraj, A.
Copyright Year	2013
Description	Author affiliation: Ibri Coll., Ibri, Oman (Izarukku, Z.) \|\| Adv. Sci. & Technol. Res. Centre Int., Salem, India (Rajendran, K.) \|\| Mahendra Eng. Coll., Namakkal, India (Subramanian, S.; Samraj, A.)
Abstract	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.
Sponsorship	IEEE United Arab Emirates Sect.
Starting Page	249
Ending Page	253
File Size	777371
Page Count	5
File Format	PDF
DOI	10.1109/CTIT.2013.6749512
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-11
Publisher Place	United Arab Emirates
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Singular Value Decomposition Three-dimensional displays Random Average Distribution Abstracts Wearable computing Data Glove Gesture computing Robot sensing systems Feature extraction Matrix decomposition Acceleration
Content Type	Text
Resource Type	Article

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