Real-time Electromyography-driven Functional Electrical Stimulation Cycling System for Chronic Stroke Rehabilitation

Volitional EMG Controlled Wearable FES System for Lower Limb Rehabilitation

Muscle rehabilitation by functional electrical stimulation (FES) is one of the effective treatments for the patients with neuromuscular diseases. The conventional FES applications, however, have limitations that utilize predetermined or repetitive stimulation patterns with the help of experts such as physical therapists. Therefore, we propose a wearable FES system in which the stimulus intensity is dynamically controlled by the motion intention of user in this paper. The proposed FES system utilizes electromyography (EMG) and inertial measurement unit (IMU) sensors for estimating the motion intention regardless of electrical stimulation, and is designed for the lower limb rehabilitation. The overall system configurations including hardware and software are presented in this paper, and the system performance was tested by lower limb exercises, e.g., squat, heel lift, and gait.

Embedded Bio-Mimetic System for Functional Electrical Stimulation Controlled by Event-Driven sEMG

The analysis of the surface ElectroMyoGraphic (sEMG) signal for controlling the Functional Electrical Stimulation (FES) therapy is being widely accepted as an active rehabilitation technique for the restoration of neuro-muscular disorders. Portability and real-time functionalities are major concerns, and, among others, two correlated challenges are the development of an embedded system and the implementation of lightweight signal processing approaches. In this respect, the event-driven nature of the Average Threshold Crossing (ATC) technique, considering its high correlation with the muscle force and the sparsity of its representation, could be an optimal solution. In this paper we present an embedded ATC-FES control system equipped with a multi-platform software featuring an easy-to-use Graphical User Interface (GUI). The system has been first characterized and validated by analyzing CPU and memory usage in different operating conditions, as well as measuring the system latency (fulfilling the real-time requirements with a 140 ms FES definition process). We also confirmed system effectiveness, testing it on 11 healthy subjects: The similarity between the voluntary movement and the stimulate one has been evaluated, computing the cross-correlation coefficient between the angular signals acquired during the limbs motion. We obtained high correlation values of 0.87 ± 0.07 and 0.93 ± 0.02 for the elbow flexion and knee extension exercises, respectively, proving good stimulation application in real therapy-scenarios.