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<p>There is an inherent trade-off between the design of comfortable wearable sensors and the need for high-resolution and large-area sensing. Here we address this challenge by introducing a generative electromyography (EMG) network (GenENet), a self-supervised generative representation learning framework combined with a wearable sensor that extrapolates limited sensor inputs to reconstruct muscle activity in unseen regions not covered by the sensor. This approach allows for gathering information equivalent to those from high-density EMG sensor networks, but using a more compact, wearable device with much reduced sensor counts, without sacrificing performance. For example, a 6-channel EMG device trained on a 32-channel dataset from low-impedance polymer electrodes shows similar performance to a 32-channel device in accuracy for predicting sign language and gait dynamics, highlighting the utility of the GenENet concept to reduce wearable-sensing-system complexity while maintaining prediction quality. Such electrophysiological data are essential for many applications, including motion detection and control, but traditionally require high-density, high-resolution sensors that can be cumbersome. This concept should be applicable to other types of electrophysiological mapping application across health monitoring, prosthetics, sports and human–machine interfaces, paving the way for more comfortable and efficient wearable devices.</p>

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A simplified wearable device powered by a generative EMG network for hand-gesture recognition and gait prediction

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Kyun Kyu Kim,
  • Tomasz J. Zaluska,
  • Six Skov,
  • Yeongjun Lee,
  • Hyunchang Park,
  • Donglai Zhong,
  • Muhammad Khatib,
  • Yuya Nishio,
  • Yuanwen Jiang,
  • Scott L. Delp,
  • Zhenan Bao

摘要

There is an inherent trade-off between the design of comfortable wearable sensors and the need for high-resolution and large-area sensing. Here we address this challenge by introducing a generative electromyography (EMG) network (GenENet), a self-supervised generative representation learning framework combined with a wearable sensor that extrapolates limited sensor inputs to reconstruct muscle activity in unseen regions not covered by the sensor. This approach allows for gathering information equivalent to those from high-density EMG sensor networks, but using a more compact, wearable device with much reduced sensor counts, without sacrificing performance. For example, a 6-channel EMG device trained on a 32-channel dataset from low-impedance polymer electrodes shows similar performance to a 32-channel device in accuracy for predicting sign language and gait dynamics, highlighting the utility of the GenENet concept to reduce wearable-sensing-system complexity while maintaining prediction quality. Such electrophysiological data are essential for many applications, including motion detection and control, but traditionally require high-density, high-resolution sensors that can be cumbersome. This concept should be applicable to other types of electrophysiological mapping application across health monitoring, prosthetics, sports and human–machine interfaces, paving the way for more comfortable and efficient wearable devices.