UltraGlove: Hand Pose Estimation with Mems-Ultrasonic Sensors

Qiang Zhang, Yuanqiao Lin, Yubin Lin, Szymon Rusinkiewicz

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 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.

Original languageEnglish (US)
Title of host publicationProceedings - SIGGRAPH Asia 2023 Conference Papers, SA 2023
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9798400703157
StatePublished - Dec 10 2023
Event2023 SIGGRAPH Asia 2023 Conference Papers, SA 2023 - Sydney, Australia
Duration: Dec 12 2023Dec 15 2023

Publication series

NameProceedings - SIGGRAPH Asia 2023 Conference Papers, SA 2023


Conference2023 SIGGRAPH Asia 2023 Conference Papers, SA 2023

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Software
  • Computer Vision and Pattern Recognition


  • Data Glove
  • Hand Tracking


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