Supervised Machine Learning Hand Gesture Classification in VR for Immersive Training

The fast adoption of immersive wearables evidences the need for more intuitive interaction methods between virtual environments and their users. Advances in wearables are making in-built real-time hand tracking mechanisms more common. However, wearable providers only include limited gestures available for developer use. This limits their use for VR-based training solutions, where users need to complete tasks that mimic real-world activities as much as possible to gain valuable insights on those tasks. We present a virtual reality application that collects data on hand characteristics and analyses the collected data to identify hand gestures towards achieving a more natural interaction.