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The prediction of gestures in shared public spaces is important not only to ensure good functionality of agents but also the safety of humans and their acceptance of the hybrid society concept. In this context, surface electromyography (sEMG) combined with inertial measurement unit (IMU) signals is investigated in this work to predict dynamic hand gestures. Our proposed algorithm is designed to make predictions, while these signals are being observed, combining a Nadaraya–Watson kernel estimator and an entropy-based decision function. We not only achieve a high accuracy, but also a significant earliness of prediction.

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Nadaraya–Watson Time Series Early Classification for Gesture Recognition

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Florian Kretzschmar,
  • Rim Barioul,
  • Dana Uhlig,
  • Olfa Kanoun,
  • Alois Pichler

摘要

The prediction of gestures in shared public spaces is important not only to ensure good functionality of agents but also the safety of humans and their acceptance of the hybrid society concept. In this context, surface electromyography (sEMG) combined with inertial measurement unit (IMU) signals is investigated in this work to predict dynamic hand gestures. Our proposed algorithm is designed to make predictions, while these signals are being observed, combining a Nadaraya–Watson kernel estimator and an entropy-based decision function. We not only achieve a high accuracy, but also a significant earliness of prediction.