Hand tracking is an important aspect of human-computer interaction and has a wide range of applications in extended reality devices. However, current hand motion capture methods suffer from various limitations. For instance, visual-based hand pose estimation is susceptible to self-occlusion and changes in lighting conditions, while IMU-based tracking gloves experience significant drift and are not resistant to external magnetic field interference. To address these issues, we propose a novel and low-cost hand-tracking glove that utilizes several MEMS-ultrasonic sensors attached to the fingers, to measure the distance matrix among the sensors. Our lightweight deep network then reconstructs the hand pose from the distance matrix. Our experimental results demonstrate that this approach is both accurate, size-agnostic, and robust to external interference. We also show the design logic for the sensor selection, sensor configurations, circuit diagram, as well as model architecture.

翻译：手部追踪是人机交互的重要方面，在扩展现实设备中具有广泛的应用。然而，当前的手部运动捕捉方法存在各种局限性。例如，基于视觉的手部姿态估计易受自遮挡和光照条件变化的影响，而基于IMU的追踪手套则存在显著漂移且无法抵抗外部磁场干扰。为解决这些问题，我们提出了一种新颖且低成本的手部追踪手套，该手套利用附着在手指上的多个MEMS超声波传感器来测量传感器间的距离矩阵。随后，我们的轻量级深度网络从距离矩阵中重建手部姿态。实验结果表明，该方法准确、不受尺寸影响，且对外部干扰具有鲁棒性。我们还展示了传感器选择、传感器配置、电路图以及模型架构的设计逻辑。