Development of a Novel Robotic Rehabilitation System With Muscle-to-Muscle Interface

Abstract

In this study, we developed a novel robotic system with a muscle-to-muscle interface to enhance rehabilitation of post-stroke patients. The developed robotic rehabilitation system was designed to provide patients with stage appropriate physical rehabilitation exercise and muscular stimulation. Unlike the position-based control of conventional bimanual robotic therapies, the developed system stimulates the activities of the target muscles, as well as the joint movements of the paretic limb. The robot-assisted motion and the electrical stimulation on the muscles of the paretic side are controlled by on-line comparison of the motion and the muscle activities between the paretic and unaffected sides. With the developed system, the rehabilitation exercise can be customized and modulated depending on the patient's stage of motor recovery after stroke. The system can be operated in three different modes allowing both passive and active exercises. The effectiveness of the developed system was verified with healthy human subjects, where the subjects were paired to serve as the unaffected side and the paretic side of a hemiplegic patient.

Lower limb rehabilitation exoskeleton robot: A review

“…Functional electrical stimulation (FES) could stimulate muscles to produce joint movement, or to stimulate the lower limbs in motion to cooperate with exoskeleton. 81,82 The overall physical condition of patients with FES assisted training has been significantly improved: Hwan et al 83 combined robot assisted exercise with FES, the system can be operated in three different modes allowing both passive and active exercises, it can better protect the user from secondary injury. Ama et al 84 designed a dynamic hybrid exoskeleton called Kinesis that can effectively balance muscular and robotic actuation during walking.…”

Machine-learning-based coordination of powered ankle–foot orthosis and functional electrical stimulation for gait control

“…Volitional muscle activity can be measured using EMG by filtering out artifacts that appear when FES and EMG are applied to the same muscle. Several studies have suggested the use of calculation-based filters to remove artifacts ( Langzam et al, 2006 ; Erez et al, 2010 ; Bong et al, 2020 ). These studies investigated methods for suppressing large artifacts induced by FES.…”

“…EMG is conventionally used to measure the muscle activity. Many studies have employed EMG to trigger and control the motion of exoskeletons ( Frigo et al, 2000 ; Del-Ama et al, 2014 ; Bong et al, 2020 ; Yin et al, 2020 ). Del-Ama et al (2014) proposed an EMG-based control system that is capable of maintaining motion-tracking performance by compensating for muscle fatigue.…”

“…When FES is applied in conjunction with EMG measurements, its electrical signals induce artifacts in the EMG signal. To extract the motion intention of the patient, the volitional EMG signal must be acquired from the raw EMG data by using artifact removal filters ( Frigo et al, 2000 ; Bong et al, 2020 ). Recently, machine-learning techniques have been introduced to estimate the volitional EMG and predict the volitional joint torques generated by patients ( Langzam et al, 2006 ; Zhou et al, 2020 ).…”

Rehabilitasyon Hizmetlerinde Yapay Zekâ Uygulamaları

“…Robot yardımlı hareket ve paretik taraf kaslarındaki elektriksel uyarı, paretik ve etkilenmemiş taraflar arasındaki hareket ve kas aktivitelerinin online karşılaştırılmasıyla kontrol edilebilmektedir. Geliştirilen bu sistem ile rehabilitasyon egzersizi, hastanın inme sonrası motor toparlanma aşamasına bağlı olarak özelleştirilebilir ve modüle edilebilir (Bong et al, 2020;Lo et al, 2017).…”