Background. Electrode arrays can simplify the modulation of shape, size, and position for customized stimulation delivery. However, the intricacy in achieving the desired outcome stems from optimizing for the myriad of possible electrode combinations and stimulation parameters to account for varying physiology across users.Objective. This study reviews automated calibration algorithms that perform such an optimization to realize hand function tasks. Comparing such algorithms for their calibration effort, functional outcome, and clinical acceptance can aid with the development of better algorithms and address technological challenges in their implementation.Methods. A systematic search was conducted across major electronic databases to identify relevant articles. The search yielded 36 suitable articles; among them, 14 articles that met the inclusion criteria were considered for the review.Results. Studies have demonstrated the realization of several hand function tasks and individual digit control using automatic calibration algorithms. These algorithms significantly improved calibration time and functional outcomes across healthy and people with neurological deficits. Also, electrode profiling performed via automated algorithms was very similar to a trained rehabilitation expert. Additionally, emphasis must be given to collecting subject-specific a priori data to improve the optimization routine and simplify calibration effort.Conclusion. With significantly shorter calibration time, delivering personalized stimulation, and obviating the need for an expert, automated algorithms demonstrate the potential for home-based rehabilitation for improved user independence and acceptance.
Keywords: calibration; cost function; functional electrical stimulation; hand function; machine learning; virtual electrode.
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