Decision support systems for lower limb rehabilitation using electrical stimulation—A review

This paper presents a comprehensive review of Decision Support Systems (DSS) for lower limb rehabilitation using Electrical Stimulation (ES), employing a rigorous two-part methodology. The first part involves a bibliometric analysis of articles from 1980 to 2023, while the second part is a systematic review of studies from 2019 to 2023, addressing six key research questions. The review identifies the main characteristics of DSS, such as data usage, sensitive data protection, reasoning techniques, and validation processes. It highlights the development focus on joint control systems, increasing interest in biofeedback and AI applications, and significant interest in FES-Cycling. Despite advancements, “decision support” remains in the early stages with simple architectures and limited data handling. Conversely, studies show advanced ES control models validated with neurological patients. This article emphasizes the need for sophisticated DSS that integrate data protection, reasoning methods, and patient monitoring to enhance rehabilitation outcomes and identifies significant gaps for future research. • Publications on rehabilitation systems with electrical stimulation for lower limbs have increased significantly. • Most studies use angle sensors and power meters to control electrical stimulation. • Biofeedback with sensors, such as EMG, is an emerging trend enabled by AI and advanced mathematical models. • None of the analyzed studies presents a complete DSS, data protection, or integration with medical records, highlighting a significant barrier to clinical viability.