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A Microcontroller Platform for the Rapid Prototyping of Functional Electrical Stimulation‐Based Gait Neuroprostheses
Author(s) -
Luzio de Melo Paulo,
da Silva Miguel Tavares,
Martins Jorge,
Newman Dava
Publication year - 2015
Publication title -
artificial organs
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.684
H-Index - 76
eISSN - 1525-1594
pISSN - 0160-564X
DOI - 10.1111/aor.12400
Subject(s) - functional electrical stimulation , microcontroller , neuroprosthetics , flexibility (engineering) , modular design , foot drop , computer science , embedded system , rapid prototyping , dc motor , simulation , engineering , medicine , physical medicine and rehabilitation , electrical engineering , mathematics , stimulation , mechanical engineering , statistics , operating system
Functional electrical stimulation ( FES ) has been used over the last decades as a method to rehabilitate lost motor functions of individuals with spinal cord injury, multiple sclerosis, and post‐stroke hemiparesis. Within this field, researchers in need of developing FES ‐based control solutions for specific disabilities often have to choose between either the acquisition and integration of high‐performance industry‐level systems, which are rather expensive and hardly portable, or develop custom‐made portable solutions, which despite their lower cost, usually require expert‐level electronic skills. Here, a flexible low‐cost microcontroller‐based platform for rapid prototyping of FES neuroprostheses is presented, designed for reduced execution complexity, development time, and production cost. For this reason, the A rduino open‐source microcontroller platform was used, together with off‐the‐shelf components whenever possible. The developed system enables the rapid deployment of portable FES ‐based gait neuroprostheses, being flexible enough to allow simple open‐loop strategies but also more complex closed‐loop solutions. The system is based on a modular architecture that allows the development of optimized solutions depending on the desired FES applications, even though the design and testing of the platform were focused toward drop foot correction. The flexibility of the system was demonstrated using two algorithms targeting drop foot condition within different experimental setups. Successful bench testing of the device in healthy subjects demonstrated these neuroprosthesis platform capabilities to correct drop foot.