Despite significant advances in gesture recognition technology, recognizing
gestures in a driving environment remains challenging due to limited and costly
data and its dynamic, ever-changing nature. In this work, we propose a
model-adaptation approach to personalize the training of a CNNLSTM model and
improve recognition accuracy while reducing data requirements. Our approach
contributes to the field of dynamic hand gesture recognition while driving by
providing a more efficient and accurate method that can be customized for
individual users, ultimately enhancing the safety and convenience of in-vehicle
interactions, as well as driver's experience and system trust. We incorporate
hardware enhancement using a time-of-flight camera and algorithmic enhancement
through data augmentation, personalized adaptation, and incremental learning
techniques. We evaluate the performance of our approach in terms of recognition
accuracy, achieving up to 90\%, and show the effectiveness of personalized
adaptation and incremental learning for a user-centered design.

我们提出了一种模型适应的方法来个性化训练 CNNLSTM 模型，提高识别准确性并减少数据需求，在驾驶环境中改进动态手势识别的效率和准确性，从而增强车内交互的安全性、便利性，以及驾驶员的体验和对系统的信任。通过使用飞行时间相机的硬件增强和数据增强、个性化适应以及增量学习技术的算法增强，我们评估了方法的识别准确性，达到了 90％，并展示了个性化适应和增量学习对以用户为中心的设计的有效性。