Editorial: Current Trends in Deep Learning for Movement Analysis and Prosthesis Control

Ali H. Al-Timemy, Claudio Castellini, Javier Escudero, Rami N. Khushaba, Silvia Muceli

Research output: Contribution to journalEditorialpeer-review

Abstract / Description of output

The control of assistive devices such as neuroprostheses and exoskeletons via bio-signals (electromyography (EMG), electrical impedance tomography (EIT), peripheral nerves signals (PNSs), etc.) is a promising research avenue in the field of function restoration for individuals with impaired movement (Phinyomark and Scheme, 2018). Movement analysis based on similar signals and kinematics plays an important role to improve the diagnosis and monitoring the progression of movement disorders.
Deep learning (DL) methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), can play an important role as an assistive diagnostic tool or in the control of devices, due to their potential to achieve high performance in classification, regression, and prediction tasks.
However, the widespread and practical implementation is still limited and needs to be further investigated.
This collection includes 6 articles whereof 5 explore DL methods for prosthesis control based on EMG (Ghaderi et al., 2022; Olsson et al., 2021; Campbell et al., 2021), EIT (Leins et al., 2021), or PNSs (Luu et al., 2021), and one (Lecomte et al., 2021) explored DL for kinematics analysis.
Original languageEnglish
Article number889202
Number of pages2
JournalFrontiers in Neuroscience
Publication statusPublished - 25 Apr 2022

Keywords / Materials (for Non-textual outputs)

  • Deep Learning
  • Deep neural network
  • myoelectric control
  • neuroprosthetics
  • kinematics


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  • Frontiers in Neuroscience (Journal)

    Ali H. Al-Timemy (Guest editor), Claudio Castellini (Guest editor), Javier Escudero Rodriguez (Guest editor), Rami N. Khushaba (Guest editor) & Silvia Muceli (Guest editor)

    Dec 2020Mar 2022

    Activity: Publication peer-review and editorial work typesEditorial activity

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