Virtual reality feedback helps improve walking speed in multiple sclerosis
Towards the Threshold: A Fall-Risk Anchored Pareto Framework for Virtual Reality Gait Feedback Selection for Individuals with Multiple Sclerosis
Human-Computer Interaction
Summary
People with multiple sclerosis often walk slower, which can increase their chance of falling. This study looked at how different types of virtual reality feedback affect walking speed and the effort it takes to use them. The researchers found that some feedback methods improved walking speed a lot but were tiring, while others gave good improvements with less effort. They created a way to compare these methods by balancing walking speed gains with effort needed. This helps pick the best virtual reality feedback to help people walk better without causing too much strain.
What this means in practice
- •For physical therapists: Select VR feedback methods that improve patient walking speed efficiently while minimizing cognitive and physical burden during rehabilitation.
- •For vr rehabilitation developers: Design VR gait training programs that optimize performance-improvement versus effort trade-offs based on this efficiency evaluation framework.
Authors
Nafisa Anjum, John Quarles, M. Rasel Mahmud
Abstract
Reduced walking speed in people with multiple sclerosis (MS) is associated with an increased risk of falls. However, virtual reality (VR)-based rehabilitation studies often emphasize performance improvements without considering the cognitive and physical effort required to achieve them. This study introduces a threshold-anchored efficiency framework that jointly evaluates gait performance and overall effort. A normative walking-velocity target of \(T=1.29\) m/s was derived from the mean walking speed of non-fallers in an independent MS gait dataset and used as a clinically motivated reference point. Thirty-four adults with MS were evaluated across eight VR feedback conditions spanning unimodal, bimodal, and multimodal feedback. For each condition, we quantified the proportion of the velocity gap to the target that was closed and the associated cognitive and physical burden. Pareto efficiency analysis identified five non-dominated conditions: Static Visual, Spatial Auditory, Auditory+Visual, Auditory+Vibrotactile, and Multimodal, whereas Spatial Vibrotactile and Vibrotactile+Visual were dominated. Spatial Auditory showed a favorable performance-effort trade-off, closing 72.7% of the velocity gap while maintaining below-average burden. Multimodal feedback achieved the greatest gap closure (95.6%) but also imposed the highest burden. These findings demonstrate that greater gait improvement does not necessarily correspond to greater rehabilitation efficiency and provide a quantitative framework for comparing VR feedback strategies according to both performance gains and participant burden.