Highlights
• We developed an accelerometry system to detect the motion intention of poststroke patients for triggering FES.
• A visual game module was combined with this automated FES training system.
• This system can reduce variability in compound movements produced by poststroke patients and FES.
• An optimal threshold of triggering can defined for each patient for specific tasks.
Abstract
Background
This paper describes the design and test of an automated functional electrical stimulation (FES) system for poststroke rehabilitation training. The aim of automated FES is to synchronize electrically induced movements to assist residual movements of patients.
Methods
In the design of the FES system, an accelerometry module detected movement initiation and movement performed by post-stroke patients. The desired movement was displayed in visual game module. Synergy-based FES patterns were formulated using a normal pattern of muscle synergies from a healthy subject. Experiment 1 evaluated how different levels of trigger threshold or timing affected the variability of compound movements for forward reaching (FR) and lateral reaching (LR). Experiment 2 explored the effect of FES duration on compound movements.
Results
Synchronizing FES-assisted movements with residual voluntary movements produced more consistent compound movements. Matching the duration of synergy-based FES to that of patients could assist slower movements of patients with reduced RMS errors.
Conclusions
Evidence indicated that synchronization and matching duration with residual voluntary movements of patients could improve the consistency of FES assisted movements. Automated FES training can reduce the burden of therapists to monitor the training process, which may encourage patients to complete the training.
1. Introduction
Hemiplegia is a common sequela experienced by stroke survivors; it leads to dysfunction in the upper and lower limbs. Various rehabilitation strategies have been adopted to help patients recover limb motor functions [1,2]. The methods of rehabilitation training currently adopted in clinic for poststroke patients are generally high-intensity, repetitive task-oriented paradigms that are practiced daily with outcome feedback [1]. Information on movement kinematics and muscle activation is often used to adjust the training strategy and to ensure that recovery progresses in the desired direction [3,4]. An inappropriate regimen in rehabilitation training may result in abnormal activation of muscles [4] and may lead to reduced effectiveness in motor functional recovery or even increased risk of muscle contracture and spasticity [5,6].
Functional electrical stimulation (FES) may potentially increase the effectiveness of rehabilitation training. It uses electrical stimulation to assist patients in producing physical movements [7] and to facilitate the training of patients’ voluntary muscle contraction [8]. Several studies have reported that FES improves the plasticity of the cerebral cortex and can be easily performed by therapists because it does not require extensive manual operations [9], [10], [11], [12]. Evidence suggests that FES is a useful modality for rehabilitation training with explainable neural mechanisms.
Progress has been made in FES applications to aid the recovery of motor functions in patients poststroke [13], and novel technologies have been integrated into FES paradigms, including gaming [14] and intelligence applications [15], [16], [17]. However, even though many control strategies have been developed to generate electrical stimulation patterns, these control strategies have not been widely translated into routine clinical uses [18], [19], [20], [21], [22] due to the controller is too complex, or needs to be adjusted according to the patient’s condition. Notably, a recent development in neuromotor control theory focusing on the modular organization of multiple muscle activations has led to the formulation of synergy-based FES strategies [23], [24], [25]. This approach provides a feasible solution for multi-channel FES control using residual muscle activities from the patient [23,[25], [26], [27], [28]]; and it leverages the idea that normal movement kinematics can be generated out of muscle synergies [23].
We have evaluated the synergy-based FES training paradigm in a short-term clinical intervention study. A five day of intervention using synergy-based FES was carried out in poststroke patients. The outcome of the short-term intervention was measured by changes in Fugl-Meyer scores and movement kinematics. Results of evaluations prior to and post intervention showed improvements in both Fugl-Meyer scores and movement kinematics [25]. In a subsequent analysis, synergy-based FES training demonstrated evidence in reorganizing neural circuits in the brain, which led to repairing of impaired muscle activation pattern towards the normal pattern [29].
In this study, we present a design and verification of an autotriggered FES system with a synergy-based stimulation strategy and used RMS errors to analyze the movement process of the patients for each trial by using acceleration. This automated FES training system is designed to continuously integrate with FES clinical protocol therapeutic intervention in stroke rehabilitation [30].
The automated FES training system with a gaming interface and accelerometer triggered generation of multiple channels of electrical stimulations to a group of targeted muscles. In this automated FES training system, we anticipated improved consistency of patient movements during rehabilitative training. If successful, the study will provide a training protocol that induces smaller RMS errors across movement trials.
2. Methods and materials
2.1. Design of the automated FES system
Fig. 1 presents a schematic of the components and experimental environment of the automated trigger FES system. The system was composed of a gaming device, an elbow cast including a radiofrequency identification (RFID) reader and an accelerometer, a multichannel FES system, and a computer. The software for the development of the training game (named Picking Apples) was created using Unity (version 2018.1.3f1, Unity Technologies Inc., CA, USA). For ease of operation, the RFID device and the Li-ion battery were mounted in the elbow cast. The RFID information and accelerometer data were transmitted wirelessly by Bluetooth (Fig. 1A).
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TBI Rehabilitation 