An Intelligent Smart Home Control Using Body Gestures

@inproceedings{citeulike:3035480, abstract = {This paper proposes the control of smart home environments such as lights and curtains using body gestures. We use a forward spotting scheme that executes gesture segmentation and recognition simultaneously. The start and end points of gestures are determined by zero crossing from negative to positive (or from positive to negative) of a competitive differential observation probability that is defined by the difference of observation probability between the maximal gesture and the non-gesture. We also use the sliding window and accumulative HMMs. We apply the proposed simultaneous gesture segmentation and recognition method to recognize the upperbody gestures for controlling the curtains and lights in a smart home environment. Experimental results show that the proposed method has a good recognition rate of 95.42\% for continuously changing gestures.}, address = {Washington, DC, USA}, author = {Kim, Daehwan and Kim, Daijin }, booktitle = {ICHIT '06: Proceedings of the 2006 International Conference on Hybrid Information Technology}, citeulike-article-id = {3035480}, doi = {10.1109/ICHIT.2006.75}, isbn = {0769526748}, keywords = {gesture, intelligent, iui09\_related\_work, smarthome}, pages = {439--446}, posted-at = {2008-07-22 23:25:06}, priority = {2}, publisher = {IEEE Computer Society}, title = {An Intelligent Smart Home Control Using Body Gestures}, url = {http://portal.acm.org/citation.cfm?id=1193209.1193552}, year = {2006} }

TY - CONF ID - citeulike:3035480 L3 - citeulike-article-id:3035480 TI - An Intelligent Smart Home Control Using Body Gestures T2 - ICHIT '06: Proceedings of the 2006 International Conference on Hybrid Information Technology SP - 439 EP - 446 AD - Washington, DC, USA PB - IEEE Computer Society SN - 0769526748 N2 - This paper proposes the control of smart home environments such as lights and curtains using body gestures. We use a forward spotting scheme that executes gesture segmentation and recognition simultaneously. The start and end points of gestures are determined by zero crossing from negative to positive (or from positive to negative) of a competitive differential observation probability that is defined by the difference of observation probability between the maximal gesture and the non-gesture. We also use the sliding window and accumulative HMMs. We apply the proposed simultaneous gesture segmentation and recognition method to recognize the upperbody gestures for controlling the curtains and lights in a smart home environment. Experimental results show that the proposed method has a good recognition rate of 95.42% for continuously changing gestures. KW - gesture KW - intelligent KW - iui09_related_work KW - smarthome AU - Kim, Daehwan AU - Kim, Daijin PY - 2006/// UR - http://portal.acm.org/citation.cfm?id=1193209.1193552 ER -