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DTSTAMP:20260114T163656Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T151500
DTEND;TZID=Australia/Melbourne:20231214T152500
UID:siggraphasia_SIGGRAPH Asia 2023_sess158_papers_588@linklings.com
SUMMARY:Hand Pose Estimation with Mems-Ultrasonic Sensors
DESCRIPTION:Qiang Zhang, Yuanqiao Lin, Yubin Lin, and Szymon Rusinkiewicz 
 (Princeton University)\n\nHand tracking is an important aspect of human-co
 mputer interaction and has a wide range of applications in extended realit
 y devices. However, current hand motion capture methods suffer from variou
 s limitations. For instance, visual-based hand pose estimation is suscepti
 ble to self-occlusion and changes in lighting conditions, while IMU-based 
 tracking gloves experience significant drift and are not resistant to exte
 rnal magnetic field interference. To address these issues, we propose a no
 vel 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 fro
 m the distance matrix. Our experimental results demonstrate that this appr
 oach is both accurate, size-agnostic, and robust to external interference.
  We also show the design logic for the sensor selection, sensor configurat
 ions, circuit diagram, as well as model architecture.\n\nRegistration Cate
 gory: Full Access\n\nSession Chair: Jae-Ho Nah (Sangmyung University)\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_588&sess=sess158
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