Institute of Digital Engineering, Technical University of Applied Sciences Würzburg-Schweinfurt, Ignaz-Schön-Straße 11, 97421 Schweinfurt, Germany.
Sensors (Basel). 2024 Sep 12;24(18):5919. doi: 10.3390/s24185919.
The body tracking systems on the current market offer a wide range of options for tracking the movements of objects, people, or extremities. The precision of this technology is often limited and determines its field of application. This work aimed to identify relevant technical and environmental factors that influence the performance of body tracking in industrial environments. The influence of light intensity, range of motion, speed of movement and direction of hand movement was analyzed individually and in combination. The hand movement of a test person was recorded with an Azure Kinect at a distance of 1.3 m. The joints in the center of the hand showed the highest accuracy compared to other joints. The best results were achieved at a luminous intensity of 500 lx, and movements in the x-axis direction were more precise than in the other directions. The greatest inaccuracy was found in the z-axis direction. A larger range of motion resulted in higher inaccuracy, with the lowest data scatter at a 100 mm range of motion. No significant difference was found at hand velocity of 370 mm/s, 670 mm/s and 1140 mm/s. This study emphasizes the potential of RGB-D camera technology for gesture control of industrial robots in industrial environments to increase efficiency and ease of use.
当前市场上的人体跟踪系统为跟踪物体、人员或肢体的运动提供了广泛的选择。该技术的精度通常受到限制，并决定了其应用领域。本工作旨在确定影响工业环境中人体跟踪性能的相关技术和环境因素。分别分析并组合分析了光强、运动范围、运动速度和手运动方向的影响。在 1.3 米的距离处，使用 Azure Kinect 记录测试人员的手部运动。与其他关节相比，手部中心关节的准确性最高。在 500 lx 的光强下取得了最佳效果，并且在 x 轴方向上的运动比其他方向更精确。z 轴方向的误差最大。运动范围越大，精度越低，运动范围为 100mm 时数据分散度最低。在 370mm/s、670mm/s 和 1140mm/s 的手速下未发现显著差异。本研究强调了 RGB-D 相机技术在工业环境中用于工业机器人的手势控制的潜力，以提高效率和易用性。
