Wearable robotic device for upper limb recovery and predictive algorithms to emPOWER the rehabilitation of stroke survivors: a protocol of safety and RCT study (POWER)

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Introduction Stroke remains a leading cause of long-term disability worldwide, with persistent upper limb (UL) impairment affecting approximately half of survivors. Robotic rehabilitation has shown efficacy comparable to conventional therapy, but most available devices are bulky, non-wearable, and limited to institutional settings. Wearable robotic solutions combined with artificial intelligence (AI) may overcome these barriers. The present protocol study will investigate the wearability and acceptance of a new soft, exosuit robotic device, the effect on the rehabilitation of paretic UL in stroke subjects and the role of AI for movement classification and actuation. Method The study will be conducted in a single neurorehabilitation center and divided into 2 phases: phase A including 10 adult healthy subjects and 10 stroke survivors who will wear the experimental robot device. The usability and acceptance of device will be ascertained by System Usability Scale (SUS); User Experience Questionnaire (UEQ); Technology Assisted Rehabilitation Patient Perception Questionnaire (TARPP-Q) measures and will be considered successfully completed whether SUS and UEQ exceed 68 and 0.8 score, respectively. Phase B is a randomized controlled trial comparing wearable robotic rehabilitation with conventional therapy in stroke survivors. Neurological evaluation will be executed by the National Institutes of Health Stroke Scale (NIHSS). Functional evaluation will be quantified according to International Classification of Functioning, Disability and Health (ICF) domains by Fugl Meyer Assessment Scale-Upper extremity (FMA-UE); Motricity Index; modified Ashworth Scale; Numerical Rating Scale; Box & Block Test; Frenchay Activity Index. Functional measures will be taken at baseline, at 2 weeks, at 4 weeks, at discharge, and at follow-up, except FMA-UE. AI algorithms will be developed to automatically assess movement quality and predict assistance intensity. Discussion The protocol tests a novel wearable device for the recovery of UL in stroke survivors overcoming the limitations of currently used robot equipment and promoting a new approach to rehabilitation outside the institutional setting. AI associated to robot rehabilitation could enhance the patient’s recovery and open the way to novel therapeutic practices.

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