6DMG: A new 6D motion gesture database. Motion-based control is gaining popularity, and motion gestures form a complementary modality in human-computer interactions. To achieve more robust user-independent motion gesture recognition in a manner analogous to automatic speech recognition, we need a deeper understanding of the motions in gesture, which arouses the need for a 6D motion gesture database. In this work, we present a database that contains comprehensive motion data, including the position, orientation, acceleration, and angular speed, for a set of common motion gestures performed by different users. We hope this motion gesture database can be a useful platform for researchers and developers to build their recognition algorithms as well as a common test bench for performance comparisons.

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  1. Tran, Thi Phuong Thao; Douzal-Chouakria, Ahlame; Varasteh Yazdi, Saeed; Honeine, Paul; Gallinari, Patrick: Interpretable time series kernel analytics by pre-image estimation (2020)
  2. Escalera, Sergio; Athitsos, Vassilis; Guyon, Isabelle: Challenges in multimodal gesture recognition (2016) ioport