Real-time hand gesture telerobotic system using fuzzy c-means clustering

Abstract

This paper describes a teleoperation system in which an articulated robot performs a block pushing task based on hand gesture commands sent through the Internet. A fuzzy c-means clustering method is used to classify hand postures as "gesture commands". The fuzzy recognition system was tested using 20 trials each of a 12-gesture vocabulary. Results revealed an acceptance rate of 99.6% (percent of gestures with a sufficiently large membership value to belong to at least one of the designated classifications), and a recognition accuracy of 100% (the percent of accepted gestures classified correctly). Performance times to carry out the pushing task showed rapid learning, reaching standard times within 4 to 6 trials by an inexperienced operator.