Multimodal deep learning network based hand ADLs tasks classification for prosthetics control

@article{Zhengyi2017MultimodalDL,
  title={Multimodal deep learning network based hand ADLs tasks classification for prosthetics control},
  author={Li Zhengyi and Zhou Hui and Yang Dandan and Xie Shuiqing},
  journal={2017 International Conference on Progress in Informatics and Computing (PIC)},
  year={2017},
  pages={91-95}
}
Natural control methods based on surface electromyography (sEMG) and pattern recognization are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many activities of daily living (ADLs). Difficulty results from limited sEMG signals susceptible to interference in clinical practice, it needs to synthesize hand movement and sEMG to improve classification robustness. Human hand ADLs are made of complex sequences of finger joint… CONTINUE READING

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