In this study spatially shaded PVDF was incorporated into a compression sleeve and used to create a wearable joystick which was able to identify and classify gestures made by the right hand. A multilayered feedforward neural network was used to discriminate movements of the hand at the wrist. In feedforward operation, the output voltage of the PVDF was collected using a DAQ system and used to populate an updating input vector which was fed into the network for real-time pattern classification. The network was trained using traditional backpropagation methods where the training inputs were an assortment of collected and simulated voltage patterns of three specific hand gestures and the outputs were specified vectors corresponding to said hand gestures. This training, coupled with the networks ability to generalize, allowed the network to correlate an input voltage profile with the gesture that generated it and correctly classify it.
- Aerospace Division
PVDF Based Wearable Joystick Using Gesture Recognition via Neural Networks
Van Volkinburg, KR, & Washington, GN. "PVDF Based Wearable Joystick Using Gesture Recognition via Neural Networks." Proceedings of the ASME 2014 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. Volume 2: Mechanics and Behavior of Active Materials; Integrated System Design and Implementation; Bioinspired Smart Materials and Systems; Energy Harvesting. Newport, Rhode Island, USA. September 8–10, 2014. V002T04A016. ASME. https://doi.org/10.1115/SMASIS2014-7577
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