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Adv Sci (Weinh) ; 11(17): e2304763, 2024 May.
Article in English | MEDLINE | ID: mdl-38429890

ABSTRACT

Innovative functional electrical stimulation has demonstrated effectiveness in enhancing daily walking and rehabilitating stroke patients with foot drop. However, its lack of precision in stimulating timing, individual adaptivity, and bilateral symmetry, resulted in diminished clinical efficacy. Therefore, a closed-loop wearable device network of intrinsically controlled functional electrical stimulation (CI-FES) system is proposed, which utilizes the personal surface myoelectricity, derived from the intrinsic neuro signal, as the switch to activate/deactivate the stimulation on the affected side. Simultaneously, it decodes the myoelectricity signal of the patient's healthy side to adjust the stimulation intensity, forming an intrinsically controlled loop with the inertial measurement units. With CI-FES assistance, patients' walking ability significantly improved, evidenced by the shift in ankle joint angle mean and variance from 105.53° and 28.84 to 102.81° and 17.71, and the oxyhemoglobin concentration tested by the functional near-infrared spectroscopy. In long-term CI-FES-assisted clinical testing, the discriminability in machine learning classification between patients and healthy individuals gradually decreased from 100% to 92.5%, suggesting a remarkable recovery tendency, further substantiated by performance on the functional movement scales. The developed CI-FES system is crucial for contralateral-hemiplegic stroke recovery, paving the way for future closed-loop stimulation systems in stroke rehabilitation is anticipated.


Subject(s)
Electric Stimulation Therapy , Stroke Rehabilitation , Wearable Electronic Devices , Humans , Stroke Rehabilitation/methods , Stroke Rehabilitation/instrumentation , Male , Middle Aged , Female , Electric Stimulation Therapy/methods , Electric Stimulation Therapy/instrumentation , Aged , Stroke/physiopathology , Stroke/therapy , Walking/physiology , Adult , Equipment Design , Spectroscopy, Near-Infrared/methods
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