Development of a High-Power Capacity Open Source Electrical Stimulation System to Enhance Research into FES-Assisted Devices: Validation of FES Cycling

Abstract

Since the first Cybathlon 2016, when twelve teams competed in the FES bike race, we have witnessed a global effort towards the development of stimulation and control strategies to improve FES-assisted devices, particularly for cycling, as a means to practice a recreational physical activity. As a result, a set of technical notes and research paved the way for many other studies and the potential behind FES-assisted cycling has been consolidated. However, engineering research needs instrumented devices to support novel developments and enable precise assessment. Therefore, some researchers struggle to develop their own FES-assisted devices or find it challenging to implement their instrumentation using commercial devices, which often limits the implementation of advanced control strategies and the possibility to connect different types of sensor. In this regard, we hypothesize that it would be advantageous for some researchers in our community to enjoy access to an entire open-source FES platform that allows different control strategies to be implemented, offers greater adaptability and power capacity than commercial devices, and can be used to assist different functional activities in addition to cycling. Hence, it appears to be of interest to make our proprietary electrical stimulation system an open-source device and to prove its capabilities by addressing all the aspects necessary to implement a FES cycling system. The high-power capacity stimulation device is based on a constant current topology that allows the creation of biphasic electrical pulses with amplitude, width, and frequency up to 150 mA, 1000 µs, and 100 Hz, respectively. A mobile application (Android) was developed to set and modify the stimulation parameters of up to eight stimulation channels. A proportional-integral controller was implemented for cadence tracking with the aim to improve the overall cycling performance. A volunteer with complete paraplegia participated in the functional testing of the system. He was able to cycle indoors for 45 min, accomplish distances of more than 5 km using a passive cycling trainer, and pedal 2400 m overground in 32 min. The results evidenced the capacity of our FES cycling system to be employed as a cycling tool for individuals with spinal cord injury. The methodological strategies used to improve FES efficiency suggest the possibility of maximizing pedaling duration through more advanced control techniques.

A Novel Functional Electrical Stimulation-Induced Cycling Controller Using Reinforcement Learning to Optimize Online Muscle Activation Pattern

“…The crank angle was evaluated through an incremental encoder (model LPD3806-360BM-G5-24C). The previously developed constant current topology electrical stimulation device, which is open source and was described in a previously published paper [ 9 ], was employed during the sessions. Firmware and software applications (Android app) for the algorithm presented in this paper are available as Supplementary Materials .…”

“…Finally, in order to track the desired pedaling cadence, the pulse amplitude of the different channels is modulated simultaneously by a PI controller over the instantaneous baseline stimulation pattern, which is constantly modified by the RL agent. The controller was described in previous work [ 9 ]. Thus, even though the agent is responsible for optimizing the stimulation pattern, the PI controller modulates the pulse amplitude for cadence tracking.…”

“…Several investigations have addressed closed-loop control strategies to track cadence, torque and power [ 7 , 8 , 9 ]. Besides the performance of the controller and cycling outcomes, the results obtained from investigations indicated that the choice of the strategy to be developed reflects a trade-off between what is expected from the rehabilitation goals and the level of complexity from a technological point of view.…”

Functional Electrostimulation System for a Prototype of a Human Hand Prosthesis Using Electromyography Signal Classification by Machine Learning Techniques

“…Although FES has been studied for several years, the signal's amplitude is still ambiguous, especially depending on the level of the patient's motion, which could not be complete due to the sickness condition [15,[30][31][32]. We considered a fundamental signal proposed as a reference signal satisfying a biphasic shape with a sawtooth shape of 30 Hz and a pulse width of 100 µs [15,[30][31][32]. At the same time, the trajectories of the simulated hand were obtained and used as the trajectory of reference.…”