TY - CHAP U1 - Konferenzveröffentlichung A1 - Sarkar, Ayanava A1 - Gepperth, Alexander A1 - Handmann, Uwe A1 - Kopinski, Thomas T1 - Dynamic Hand Gesture Recognition for Mobile Systems Using Deep LSTM T2 - Intelligent Human Computer Interaction. IHCI 2017. Lecture Notes in Computer Science N2 - 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 × 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. Y1 - 2017 SN - 978-3-319-72038-8 SB - 978-3-319-72038-8 U6 - https://doi.org/https://doi.org/10.1007/978-3-319-72038-8_3 DO - https://doi.org/https://doi.org/10.1007/978-3-319-72038-8_3 IS - vol. 10688 SP - 19 EP - 31 S1 - 13 PB - Springer ER -