Abstract
In this study, we proposed a novel machine-learning-based functional electrical stimulation (FES) control algorithm to enhance gait rehabilitation in post-stroke hemiplegic patients. The electrical stimulation of the muscles on the paretic side was controlled via deep neural networks, which were trained using muscle activity data from healthy people during gait. The performance of the developed system in comparison with that of a conventional FES control method was tested with healthy human subjects.
Highlights
A stroke occurs when the blood supply to the brain is blocked by an ischemic or hemorrhagic event due to a thrombus inside the blood vessel [1]
50% of stroke patients suffer from hemiplegia, a major cause of immobility, which degrades patients’ quality of life
As the patient is incapable of generating sufficient muscle force and range of motion in the paretic side, the muscle activities induced by functional electrical stimulation (FES) are designed to assist of patient motion in pareticaside, the muscle activities by FES
Summary
A stroke occurs when the blood supply to the brain is blocked by an ischemic or hemorrhagic event due to a thrombus inside the blood vessel [1]. Among the various types of mobility impairments, foot drop caused by spasticity of the ankle joint is the most common in hemiplegic patients [3,4]. To alleviate spasticity and improve functionality in post-stroke patients, many studies have been conducted on physical and neurological treatments, such as exercise therapy and electric stimulation therapy [5,6,7]. Among these treatments, functional electrical stimulation (FES) is one of the most widely used methods for the rehabilitation of poststroke hemiplegic patients. FES has an advantage over other physical rehabilitation methods in terms of treating neurological injuries, including neural plasticity [8,9,10]
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