In　recent　years,　the　use　of　human　movements,　especially　hand　gestures,　serves　as　a　motivating　force　for　research　in　gesture　modeling,　analyzing　and　recognition.　Hand　gesture　recognition　provides　an　intelligent,　natural,　and　convenient　way　of　human-robot　interaction　(HRI).　According　to　the　way　of　the　input　of　gestures,　the　current　gesture　recognition　techniques　can　be　divided　into　two　categories:　based　on　the　vision　and　based　on　the　data　gloves.　In　order　to　cope　with　some　problems　existed　in　currently　data　glove.　In　this　paper,　we　use　a　novel　data　glove　called　YoBu　to　collect　data　for　gesture　recognition.　And　we　attempt　to　use　extreme　learning　machine　(ELM)　for　gesture　recognition　which　has　not　yet　found　in　the　relevant　application.　In　addition,　we　analyzed　which　features　play　an　important　role　in　classification　and　collect　data　of　static　gestures　as　well　as　establish　a　gesture　dataset.