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. 2021 Dec 16:15:750519.
doi: 10.3389/fnbot.2021.750519. eCollection 2021.

Adaptation Strategies for Personalized Gait Neuroprosthetics

Anne D Koelewijn  1 , Musa Audu  2   3 , Antonio J Del-Ama  4 , Annalisa Colucci  5 , Josep M Font-Llagunes  6   7 , Antonio Gogeascoechea  8 , Sandra K Hnat  2   3 , Nathan Makowski  2   9 , Juan C Moreno  10 , Mark Nandor  2   11 , Roger Quinn  2   11 , Marc Reichenbach  12   13 , Ryan-David Reyes  2   3 , Massimo Sartori  8 , Surjo Soekadar  5 , Ronald J Triolo  2   3 , Mareike Vermehren  5 , Christian Wenger  14 , Utku S Yavuz  15 , Dietmar Fey  13 , Philipp Beckerle  16
Affiliations

Adaptation Strategies for Personalized Gait Neuroprosthetics

Anne D Koelewijn et al. Front Neurorobot. .

Abstract

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.

Keywords: embedded artificial intelligence; neural interface; neuroprosthesis; personalized devices; perspective; resistive random access memory.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Overview of the different organs involved in gait and their communication structure (left). The right side shows examples of prostheses that interface with these organs, specifically the brain (top), spinal cord (bottom left), or periphery (bottom right).
Figure 2
Figure 2
Overview of the topics of this perspective on gait neuroprostheses. We discuss requirements and future directions for interfaces with the periphery and the central nervous system (brain and spinal cord), as well as for the computer architecture.
See this image and copyright information in PMC

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