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Despite significant advances in gesture recognition technology, recognizinggestures in a driving environment remains challenging due to limited and costlydata and its dynamic, ever-changing nature. In this work, we propose amodel-adaptation approach to personalize the training of a CNNLSTM model andimprove recognition accuracy while reducing data requirements. Our approachcontributes to the field of dynamic hand gesture recognition while driving byproviding a more efficient and accurate method that can be customized forindividual users, ultimately enhancing the safety and convenience of in-vehicleinteractions, as well as driver's experience and system trust. We incorporatehardware enhancement using a time-of-flight camera and algorithmic enhancementthrough data augmentation, personalized adaptation, and incremental learningtechniques. We evaluate the performance of our approach in terms of recognitionaccuracy, achieving up to 90\%, and show the effectiveness of personalizedadaptation and incremental learning for a user-centered design.

It's all about you: Personalized in-Vehicle Gesture Recognition with a Time-of-Flight Camera