@inproceedings{Wolf:2016:MDR:2957265.2961865b,
 abstract = {The rise of smart rings enables for ubiquitous control of computers that are wearable or mobile. We developed a ring interface using a 9 DOF IMU for detecting microgestures that can be executed while performing another task that involve hands, e.g. riding a bicycle. For the gesture classification we implemented 4 classifiers that run on the Android operating system without the need of clutch events. In a user study, we compared the success of 4 classifiers in a cycling scenario. We found that Random Forest (RF) works better for microgesture detection on Android than Dynamic Time Warping (DTW), K-Nearest-Neighbor (KNN), and than a Threshold (TH)-based approach as it has the best detection rate while it runs in real-time on Android. This work shell encourages other researchers to develop further mobile applications for using remote microgesture control in encumbered contexts.},
 address = {Florence, Italy},
 author = { Katrin Wolf and Sven Mayer and Stephan Meyer},
 booktitle = {Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct},
 date = {2016-01-01},
 doi = {10.1145/2957265.2961865},
 isbn = {978-1-4503-4413-5},
 keywords = {bio-mechanic, encumbered contexts, ergonomics, gesture, microgesture},
 pages = {783--790},
 publisher = {ACM},
 pubstate = {published},
 series = {MobileHCI '16},
 title = {Microgesture Detection for Remote Interaction with Mobile Devices},
 tppubtype = {inproceedings},
 url = {http://sven-mayer.com/wp-content/uploads/2017/03/wolf2016microgesture.pdf},
 year = {2016}
}