UltraGlove: Hand Pose Estimation with Mems-Ultrasonic Sensors
This addresses hand tracking limitations for human-computer interaction in extended reality, offering a solution to issues like occlusion and drift, though it appears incremental as it builds on existing sensor-based approaches.
The paper tackles hand pose estimation by proposing a low-cost glove with MEMS-ultrasonic sensors to measure distances between fingers, using a deep network for reconstruction, resulting in an accurate, size-agnostic, and robust method.
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.