A hybrid orthosis combining functional electrical stimulation and soft robotics for improved assistance of drop-foot. (May 2023)

• Record Type: Journal Article
• Authors: Hodgins, Lucy; Freeman, Chris T.
• Is Part Of: Medical engineering & physics. Volume 115(2023)
• Publication Date: 2023-05
• Subjects: Drop foot -- Soft robotics -- Electrical stimulation -- Feedback control -- Assistive technology -- Hybrid orthosis; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2023.103979
• Languages: English
• ISSNs: 1350-4533

Abstract

Abstract: Drop-foot is characterised by an inability to lift the foot, and affects an estimated 3 million people worldwide. Current treatment methods include rigid splints, electromechanical systems, and functional electrical stimulation (FES). However, these all have limitations, with electromechanical systems being bulky and FES leading to muscle fatigue. This paper addresses the limitations with current treatments by developing a novel orthosis combining FES with a pneumatic artificial muscle (PAM). It is the first system to combine FES and soft robotics for application to the lower limb, as well as the first to employ a model of their interaction within the control scheme. The system embeds a hybrid controller based on model predictive control (MPC), which combines FES and PAM components to optimally balance gait cycle tracking, fatigue reduction and pressure demands. Model parameters are found using a clinically feasible model identification procedure. Experimental evaluation using the system with three healthy subjects demonstrated a reduction in fatigue compared with the case of only using FES, which is supported by numerical simulation results. Highlights: A drop foot device is developed that combines a pneumatic artificial muscle and functional electrical stimulation (FES) for the first time. The combined system improves gait tracking compared to either approach used individually. Combining soft robotics and FES reduces muscle fatigue when compared to purely FES. Model predictive control can be used to optimize control parameters.