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CST Framework: A Robust and Portable Finger Motion Tracking Framework
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AI
DOI：10.1109/THMS.2024.3385105.png)
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Finger motion tracking is a significant challenge in the field of motion capture. However, existing technology for finger motion tracking often requires the wearing of a heavy device and a laborious calibration process to track the bending angle of each joint; this can be challenging, particularly because the motion of each finger has a high coupling characteristic. To address this issue, in this work, we have proposed a compressed sensing-based tracking (CST) framework that enables the estimation of the bending angle of all hand joints using sensors smaller than the number of hand joints. Our framework also integrates a real-time calibration function, which significantly simplifies the calibration process. We developed a glove with multiple liquid metal sensors and an inertial measurement unit to evaluate the effectiveness of our CST framework. The experimental results show that our CST framework can achieve high-speed and accurate hand arbitrary motion capture with only 12 sensors. The motion-tracking gloves developed on this basis are user-friendly and particularly suitable for human-computer interaction applications in robot control, the metaverse and other fields.
Keyword:
Physical human-robot interaction
sensor-based control
virtual reality and interfaces
wearable robots
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Design of an Inertial-Sensor-Based Data Glove for Hand Function Evaluation基于惯性传感器的手部功能评估数据手套的设计.png)
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