The Development of a Closed-Loop, Patient-Centered, Extended Reality System to Enhance Balance in People with Multiple Sclerosis: A Research Protocol

Background: Falls are common and harmful in people with multiple sclerosis (PwMS), especially during daily movements. Stepping impairments contribute significantly to fall risk but stepping-focused interventions are limited and rarely target those with higher disability. Mixed reality (MR) offers a promising, adaptive, and safe solution for stepping training even without supervision. This project aims to develop and test a closed-loop, AR-based system to assess and improve dynamic balance in PwMS. Methods: This observational study on PwMS consists of three milestones: (1) development and validation of a near-fall detection algorithm using head-worn sensors during multidirectional stepping; (2) design of an MR enviroment for personalized, adaptive, stepping training; and (3) testing the usability, reliability, and neural responses of the MR system in PwMS and healthy controls. Results: This project will advance precision rehabilitation in PwMsby developing the first closed-loop MR system for unsupervised, personalized multidirectional stepping training and assessment. Discussion: Volitional stepping training should be a core component on effective fall prevention interventions in PwMS and its effectiveness can be further enhanced through an head mounted display MR system.