A multimodal gesture recognition dataset for desktop human-computer
  interaction
Qi Wang Fengchao Zhu Guangming Zhu Liang Zhang Ning Li Eryang Gao
Gesture recognition is an indispensable component of natural and efficient
human-computer interaction technology, particularly in desktop-level
applications, where it can significantly enhance people's productivity.
However, the current gesture recognition community lacks a suitable
desktop-level (top-view perspective) dataset for lightweight gesture capture
devices. In this study, we have established a dataset named GR4DHCI. What
distinguishes this dataset is its inherent naturalness, intuitive
characteristics, and diversity. Its primary purpose is to serve as a valuable
resource for the development of desktop-level portable applications. GR4DHCI
comprises over 7,000 gesture samples and a total of 382,447 frames for both
Stereo IR and skeletal modalities. We also address the variances in hand
positioning during desktop interactions by incorporating 27 different hand
positions into the dataset. Building upon the GR4DHCI dataset, we conducted a
series of experimental studies, the results of which demonstrate that the
fine-grained classification blocks proposed in this paper can enhance the
model's recognition accuracy. Our dataset and experimental findings presented
in this paper are anticipated to propel advancements in desktop-level gesture
recognition research.