Combining robotics and functional electrical stimulation for assist-as-needed support of leg movements in stroke patients: A feasibility study.

PURPOSE: Rehabilitation technology can be used to provide intensive training in the early phases after stroke. The current study aims to assess the feasibility of combining robotics and functional electrical stimulation (FES), with an assist-as-needed approach to support actively-initiated leg movements in (sub-)acute stroke patients. METHOD: Nine subacute stroke patients performed repetitions of ankle dorsiflexion and/or knee extension movements, with and without assistance. The assist-as-needed algorithm determined the amount and type of support needed per repetition. The number of repetitions and range of motion with and without assistance were compared with descriptive statistics. Fatigue scores were obtained using the visual analogue scale (score 0-10). RESULTS: Support was required in 44 % of the repetitions for ankle dorsiflexion and in 5 % of the repetitions of knee extension, The median fatigue score was 2.0 (IQR: 0.2) and 4.0 (IQR: 1.5) for knee and ankle, respectively, indicating mild to moderate perceived fatigue. CONCLUSION: This study demonstrated the feasibility of assist-as-needed assistance through combined robotic and FES support of leg movements in stroke patients. It proved particularly useful for ankle dorsiflexion. Future research should focus on implementing this approach in a clinical setting, to assess clinical applicability and potential effects on leg function.

A robot-based hybrid lower limb system for Assist-As-Needed rehabilitation of stroke patients: Technical evaluation and clinical feasibility.

BACKGROUND: Although early rehabilitation is important following a stroke, severely affected patients have limited options for intensive rehabilitation as they are often bedridden. To create a system for early rehabilitation of lower extremities in these patients, we combined the robotic manipulator ROBERT® with electromyography (EMG)-triggered functional electrical stimulation (FES) and developed a novel user-driven Assist-As-Needed (AAN) control. The method is based on a state machine able to detect user movement capability, assessed by the presence of an EMG-trigger and the movement velocity, and provide different levels of assistance as required by the patient (no support, FES only, and simultaneous FES and mechanical assistance). METHODS: To technically validate the system, we tested 10 able-bodied participants who were instructed to perform specific behaviors to test the system states while conducting knee extension and ankle dorsal flexion exercises. The system was also tested on two stroke patients to establish its clinical feasibility. RESULTS: The technical validation showed that the state machine correctly detected the participants' behavior and activated the target AAN state in more than 96% of the exercise repetitions. The clinical feasibility test showed that the system successfully recognized the patients' movement capacity and activated assistive states according to their needs providing the minimal level of support required to exercise successfully. CONCLUSIONS: The system was technically validated and preliminarily proved clinically feasible. The present study shows that the novel system can be used to deliver exercises with a high number of repetitions while engaging the participants' residual capabilities through the AAN strategy.