In this paper, a low-cost wearable hand gesture detecting system based on distributed multi-node inertial measurement units (IMUs) and central node microcontroller is presented. It can obtain hand kinematic information and transmit data to the remote processing terminal wirelessly. To have a comprehensive understanding of hand kinematics, a convolutional neural network (CNN) model on the terminal is proposed to recognize and classify gestures and the modified Denavit-Hartenberg notation is used to acquire finger spatial locations. The experiment has not only completed a variety of gesture recognitions, but also captured and displayed the orientation and posture of a single finger. The prototype can be used in various occasions such as hand rehabilitation evaluation and human-computer interaction.
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| http://dx.doi.org/10.1109/EMBC46164.2021.9630686 | DOI Listing |
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