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- W4361761698 abstract "The last few decades have witnessed an emerging trend of wearable soft sensors; however, there are important signal-processing challenges for soft sensors that still limit their practical deployment. They are error-prone when displaced, resulting in significant deviations from their ideal sensor output. In this work, we propose a novel prototype that integrates an elbow pad with a sparse network of soft sensors. Our prototype is fully bio-compatible, stretchable, and wearable. We develop a learning-based method to predict the elbow orientation angle and achieve an average tracking error of 9.82 degrees for single-user multi-motion experiments. With transfer learning, our method achieves the average tracking errors of 10.98 degrees and 11.81 degrees across different motion types and users, respectively. Our core contributions lie in a solution that realizes robust and stable human joint motion tracking across different device displacements." @default.
- W4361761698 created "2023-04-04" @default.
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- W4361761698 date "2022-03-27" @default.
- W4361761698 modified "2023-09-24" @default.
- W4361761698 title "DisPad" @default.
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- W4361761698 doi "https://doi.org/10.1145/3580832" @default.
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