Bilateral control of functional electrical stimulation and robotics-based telerehabilitation

Abstract

Currently a telerehabilitation system includes a therapist and a patient where the therapist interacts with the patient, typically via a verbal and visual communication, for assessment and supervision of rehabilitation interventions. This mechanism often fails to provide physical assistance, which is a modus operandi during physical therapy or occupational therapy. Incorporating an actuation modality such as functional electrical stimulation (FES) or a robot at the patient’s end that can be controlled by a therapist remotely, to provide therapy or to assess and measure rehabilitation outcomes can significantly transform current telerehabilitation technology. In this paper, a position-synchronization controller is derived for FES-based telerehabilitation to provide physical assistance that can be controlled remotely. The newly derived controller synchronizes an FES-driven human limb with a remote physical therapist’s robotic manipulator despite constant bilateral communication delays. The control design overcomes a major stability analysis challenge: the unknown and unstructured nonlinearities in the FES-driven musculoskeletal dynamics. To address this challenge, the nonlinear muscle model was estimated through two neural networks functions that approximated unstructured nonlinearities and an adaptive control law for structured nonlinearities with online update laws. A Lyapunov-based stability analysis was used to prove the globally uniformly ultimately bounded tracking performance. The performance of the state synchronization controller was validated through experiments on an able-bodied subject. Specifically, we demonstrated bilateral control of FES-elicited leg extension and a human operated robotic manipulator. The controller was shown to effectively synchronize the system despite unknown and different delays in the forward and backward channels.

Predictor-based control of time-delay systems: a survey

“…The two aforementioned approaches were implemented on several practical systems. In (Sharma, Gregory, & Dixon, 2011;Qiu, Alibeji, & Sharma, 2016), the robust predictive controllers were applied to neuromuscular rehabilitation systems, and a similar delay compensation technique was further designed for musculoskeletal wearable systems (Alibeji, Dicianno, & Sharma, 2017;Alibeji, Kirsch, & Sharma, 2017;Sheng, Sun, Molazadeh, & Sharma, 2021). The second method (62)-( 63) handled the accurate grasp-place control of a 7-DoF Baxter manipulator (Bagheri et al, 2018(Bagheri et al, , 2019b(Bagheri et al, , 2019b.…”

Closed-Loop Torque and Kinematic Control of a Hybrid Lower-Limb Exoskeleton for Treadmill Walking

“…Cooperative control between motor and muscle loops has been developed to minimize the motor torque contribution and maximize the muscle-generated joint torques via surface stimulation ( Ha et al, 2016 ). A position-based controller combining neural networks and classical adaptive control was designed to synchronize a robotic manipulator and FES during assisted leg extension ( Alibeji et al, 2017 ). A closed-loop adaptive control design using iterative learning and neural networks was developed to distribute the control between FES and electric motors to perform sit-to-stand tasks ( Molazadeh et al, 2021 ).…”

Bilateral Control of Elbow and Shoulder Joints Using Functional Electrical Stimulation Between Humans and Robots

“…When a bilateral control system is constructed between a human, who is controlled by FES and acts as a master, and a robot (slave), the reaction force received by the robot can be transmitted while the human feels lesser restraint. Conventional studies have proposed bilateral control between humans and robots using FES in the hand, elbow joint, and foot [16]- [18]. It is more useful in the master system with multiple degrees of freedom (DoF) such as shoulders and fingers, because mechanical structures to display multi DoF reaction force is almost impossibly difficult.…”