A new modular neuroprosthesis suitable for hybrid FES-robot applications and tailored assistance.

Background: To overcome the application limitations of functional electrical stimulation (FES), such as fatigue or nonlinear muscle response, the combination of neuroprosthetic systems with robotic devices has been evaluated, resulting in hybrid systems that have promising potential. However, current technology shows a lack of flexibility to adapt to the needs of any application, context or individual. The main objective of this study is the development of a new modular neuroprosthetic system suitable for hybrid FES-robot applications to meet these needs.

Methods: In this study, we conducted an analysis of the requirements for developing hybrid FES-robot systems and reviewed existing literature on similar systems. Building upon these insights, we developed a novel modular neuroprosthetic system tailored for hybrid applications. The system was specifically adapted for gait assistance, and a technological personalization process based on clinical criteria was devised. This process was used to generate different system configurations adjusted to four individuals with spinal cord injury or stroke. The effect of each system configuration on gait kinematic metrics was analyzed by using repeated measures ANOVA or Friedman's test.

Results: A modular NP system has been developed that is distinguished by its flexibility, scalability and personalization capabilities. With excellent connection characteristics, it can be effectively integrated with robotic devices. Its 3D design facilitates fitting both as a stand-alone system and in combination with other robotic devices. In addition, it meets rigorous requirements for safe use by incorporating appropriate safety protocols, and features appropriate battery autonomy, weight and dimensions. Different technological configurations adapted to the needs of each patient were obtained, which demonstrated an impact on the kinematic gait pattern comparable to that of other devices reported in the literature.

Conclusions: The system met the identified technical requirements, showcasing advancements compared to systems reported in the literature. In addition, it demonstrated its versatility and capacity to be combined with robotic devices forming hybrids, adapting well to the gait application. Moreover, the personalization procedure proved to be useful in obtaining various system configurations tailored to the diverse needs of individuals.