gesture recognition

This dataset provides valuable insights into hand gestures and their associated measurements. Hand gestures play a significant role in human communication, and understanding their patterns and characteristics can be enabled various applications, such as gesture recognition systems, sign language interpretation, and human-computer interaction. This dataset was carefully collected by a specialist who captured snapshots of individuals making different hand gestures and measured specific distances between the fingers and the palm.

Radar-based dynamic gesture recognition has a broad prospect in the field of touchless Human-Computer Interaction (HCI) due to its advantages in many aspects such as privacy protection and all-day working. Due to the lack of complete motion direction information, it is difficult to implement existing radar gesture datasets or methods for motion direction sensitive gesture recognition and cross-domain (different users, locations, environments, etc.) recognition tasks.

Recently, surface electromyography (sEMG) emerged as a novel biometric authentication method. Since EMG system parameters, such as the feature extraction methods and the number of channels, have been known to affect system performances, it is important to investigate these effects on the performance of the sEMG-based biometric system to determine optimal system parameters.

Holoscopic micro-gesture recognition (HoMG) database was recorded using a holoscopic 3D camera, which have 3 conventional gestures from 40 participants under different settings and conditions. The principle of holoscopic 3D (H3D) imaging mimics fly’s eye technique that captures a true 3D optical model of the scene using a microlens array. For the purpose of H3D micro-gesture recognition. HoMG database has two subsets. The video subset has 960 videos and the image subset has 30635 images, while both have three type of microgestures (classes).