{"id":317726,"date":"2016-11-07T15:25:41","date_gmt":"2016-11-07T23:25:41","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=317726"},"modified":"2018-10-16T20:10:15","modified_gmt":"2018-10-17T03:10:15","slug":"making-gestural-input-arm-worn-inertial-sensors-practical","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/making-gestural-input-arm-worn-inertial-sensors-practical\/","title":{"rendered":"Making Gestural Input from Arm-Worn Inertial Sensors More Practical"},"content":{"rendered":"

Gestural input can greatly improve computing experiences away from the desktop, and has the potential to provide always-available access to computing. Specifically, accelerometers and gyroscopes worn on the arm (e.g., in a wristwatch) can sense arm gestures, enabling natural input in untethered scenarios. Two core components of any gesture recognition system are detecting when a gesture is occurring and classifying which gesture a person has performed. In previous work, accurate detection has required significant computation, and high-accuracy classification has come at the cost of training the system on a per-user basis. In this note, we present a gesture detection method whose computational complexity does not depend on the duration of the gesture, and describe a novel method for recognizing gestures with only a single example from a new user<\/p>\n","protected":false},"excerpt":{"rendered":"

Gestural input can greatly improve computing experiences away from the desktop, and has the potential to provide always-available access to computing. Specifically, accelerometers and gyroscopes worn on the arm (e.g., in a wristwatch) can sense arm gestures, enabling natural input in untethered scenarios. Two core components of any gesture recognition system are detecting when a […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13562,13554],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-317726","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"CHI '12 Proceedings of the SIGCHI Conference on Human Factors in Computing 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