基于Noitom数据手套的船舶航行仿真交互方法研究

Research on Interactive Method for Ship Navigation Simulation Based on Noitom Data Gloves

摘要:

本研究实现了基于Unity引擎的船舶航行虚拟仿真，并构建了虚拟手模型，通过数据手套和方位跟踪器驱动虚拟手模型，获得人手空间坐标数据，并将其转换为虚拟手模型的虚拟坐标数据。通过不同的虚拟手手势控制船舶的虚拟航行过程，包括进退、转向和UI射线交互。进行了六种静态手势的交互实验，并通过误差标定和卡尔曼滤波算法增强数据稳定性。实验结果表明，六种手势的平均识别成功率为97%。影响手势识别准确性的因素包括手势复杂度、传感器误差、VR设备基站位置、遮挡效应及外部干扰等。实验验证了基于Noitom数据手套的船舶航行仿真交互的可行性和有效性，提供了提升人机交互效率和便捷性的技术支持。

Abstract:

This study implemented a ship navigation virtual simulation based on the Unity engine and constructed a virtual hand model driven by a data glove and orientation tracker. The spatial coordinates of the human hand were obtained and converted into virtual coordinates of the virtual hand model. Different hand gestures were used to control the ship's virtual navigation, including forward, backward, steering, and UI ray interactions. Six static hand gestures were tested in interaction experiments, with error calibration and Kalman filtering algorithms applied to enhance data stability. The experimental results showed that the average recognition success rate for the six gestures was 97%. Factors affecting gesture recognition accuracy include gesture complexity, sensor errors, VR device base station location, occlusion effects, and external interference. The experiment validated the feasibility and effectiveness of the ship navigation simulation interaction based on the Noitom data glove, providing technical support for improving the efficiency and convenience of human-machine interaction. Key words：Inertial sensors; Gesture recognition (HGR); Human Computer Interaction (HCI); Intelligent wearable devices; Static gestures; Kalman filter algorithm