Objective: Hybrid systems that integrate Functional Electrical Stimulation (FES) and robotic assistance have been proposed in neurorehabilitation to enhance therapeutic benefits. This study focuses on designing a cooperative controller capable of distributing the required torque for movement between robotic actuation and FES, thereby eliminating the need for time-consuming calibration procedures. Methods: The control schema comprises three main blocks: a motion generation block that defines the desired trajectory, a motor control block including both a weight compensation feedforward and a feedback impedance controller, and an FES control block, based on trial-by-trial Iterative Learning Control (ILC), that adjusts the stimulation intensity according to a predefined stimulation waveform. The feedforward motor assistance can be dynamically regulated using an allocation factor. Experiments involving 12 healthy volunteers were conducted using a one-degree-of-freedom elbow testbed. Results: The experimental results showcased the successful integration of Functional Electrical Stimulation (FES) with robotic actuation, ensuring precise trajectory tracking with a Root Mean Square Error (RMSE) below 7°. Notably, allocating more torque to FES led to a 51% reduction in motor torque. In conditions where FES operated alone, there was poorer tracking performance with an RMSE of 24° and an early onset of muscle fatigue, as evidenced by a reduced number of achieved repetitions. Furthermore, the hybrid approach enabled 100 fatigue-free elbow flexion repetitions, underscoring the effectiveness of cooperative FES-motor control in extending the benefits of FES-induced exercises. Significance: This study proposes a flexible approach which can be extended to a multi-degree-of-freedom hybrid system. Furthermore, it underscores the significance of employing a straightforward and adaptable methodology with a rapid calibration procedure, making it easily transferable to clinical applications.

Background During Transcranial Magnetic Stimulation (TMS) experiments researchers often use a neuronavigation system to precisely and accurately maintain coil position and orientation. New method This study aimed to develop and validate an open-source software for TMS coil navigation. StimTrack uses an optical tracker and an intuitive user interface to facilitate the maintenance of position and orientation of any type of coil within and between sessions. Additionally, online access to navigation data is provided, hereby adding e.g. the ability to start or stop the magnetic stimulator depending on the distance to target or the variation of the orientation angles. Results StimTrack allows repeatable repositioning of the coil within 0.7 mm for translation and <1° for rotation. Stimulus-response (SR) curves obtained from 19 healthy volunteers were used to demonstrate that StimTrack can be effectively used in a typical experiment. An excellent intra and inter-session reliability (ICC > 0.9) was obtained on all parameters computed on SR curves acquired using StimTrack. Comparison with existing method StimTrack showed a target accuracy similar to that of a commercial neuronavigation system (BrainSight, Rogue Research Inc.). Indeed, small differences both in position (∼0.2 mm) and orientation (<1°) were found between the systems. These differences are negligible given the human error involved in landmarks registration. Conclusions StimTrack, available as supplementary material, is found to be a good alternative for commercial neuronavigation systems facilitating assessment changes in corticospinal excitability using TMS. StimTrack allows researchers to tailor its functionality to their specific needs, providing added value that benefits experimental procedures and improves data quality.