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.

提出了一种便携低成本的手部跟踪手套，采用多个微机电系统超声波传感器测量手指之间的距离矩阵，再利用轻量级深度网络从距离矩阵中重建出手部姿态，该方法精度高、尺寸不变且对外部干扰具有鲁棒性。