%0 Conference Paper %1 sarkar2017dynamic %A Sarkar, Ayanava %A Gepperth, Alexander %A Handmann, Uwe %A Kopinski, Thomas %B Intelligent Human Computer Interaction %C Cham %D 2017 %E Horain, Patrick %E Achard, Catherine %E Mallem, Malik %I Springer %K deep-learning gestural-interaction mobile-computing real %P 19-31 %R 10.1007/978-3-319-72038-8_3 %T Dynamic Hand Gesture Recognition for Mobile Systems Using Deep LSTM %U https://doi.org/10.1007/978-3-319-72038-8_3 %V 10688 %X We present a pipeline for recognizing dynamic freehand gestures on mobile devices based on extracting depth information coming from a single Time-of-Flight sensor. Hand gestures are recorded with a mobile 3D sensor, transformed frame by frame into an appropriate 3D descriptor and fed into a deep LSTM network for recognition purposes. LSTM being a recurrent neural model, it is uniquely suited for classifying explicitly time-dependent data such as hand gestures. For training and testing purposes, we create a small database of four hand gesture classes, each comprising 40 \$\$\backslashtimes \$\$× 150 3D frames. We conduct experiments concerning execution speed on a mobile device, generalization capability as a function of network topology, and classification ability `ahead of time', i.e., when the gesture is not yet completed. Recognition rates are high (>95\%) and maintainable in real-time as a single classification step requires less than 1 ms computation time, introducing freehand gestures for mobile systems. %@ 978-3-319-72038-8

@inproceedings{sarkar2017dynamic, abstract = {We present a pipeline for recognizing dynamic freehand gestures on mobile devices based on extracting depth information coming from a single Time-of-Flight sensor. Hand gestures are recorded with a mobile 3D sensor, transformed frame by frame into an appropriate 3D descriptor and fed into a deep LSTM network for recognition purposes. LSTM being a recurrent neural model, it is uniquely suited for classifying explicitly time-dependent data such as hand gestures. For training and testing purposes, we create a small database of four hand gesture classes, each comprising 40 {\$}{\$}{\backslash}times {\$}{\$}{\texttimes} 150 3D frames. We conduct experiments concerning execution speed on a mobile device, generalization capability as a function of network topology, and classification ability `ahead of time', i.e., when the gesture is not yet completed. Recognition rates are high (>95{\%}) and maintainable in real-time as a single classification step requires less than 1 ms computation time, introducing freehand gestures for mobile systems.}, added-at = {2019-11-14T07:01:57.000+0100}, address = {Cham}, author = {Sarkar, Ayanava and Gepperth, Alexander and Handmann, Uwe and Kopinski, Thomas}, biburl = {https://www.bibsonomy.org/bibtex/24c8b54eacafbc0f37b2f1b8b146b79e5/jpmor}, booktitle = {Intelligent Human Computer Interaction}, description = {Dynamic Hand Gesture Recognition for Mobile Systems Using Deep LSTM | SpringerLink}, doi = {10.1007/978-3-319-72038-8_3}, editor = {Horain, Patrick and Achard, Catherine and Mallem, Malik}, interhash = {3602ed6f28f25dae923e62742330a745}, intrahash = {4c8b54eacafbc0f37b2f1b8b146b79e5}, isbn = {978-3-319-72038-8}, keywords = {deep-learning gestural-interaction mobile-computing real}, language = {English}, pages = {19-31}, publisher = {Springer}, school = {Birla Institute of Technology and Science (BITS)}, series = {Lecture Notes in Computer Science}, timestamp = {2020-10-07T13:36:50.000+0200}, title = {Dynamic Hand Gesture Recognition for Mobile Systems Using Deep LSTM}, url = {https://doi.org/10.1007/978-3-319-72038-8_3}, volume = 10688, year = 2017 }