# Advancing Gait Analysis: Integrating Multimodal Neuroimaging and Extended Reality Technologies

## TL;DR

This review explores how combining brain imaging and extended reality technologies can improve the study of human walking for better diagnosis and treatment of movement disorders.

## Contribution

The paper introduces a multidisciplinary approach integrating neuroimaging, extended reality, and sensor-based methods for advanced gait analysis.

## Key findings

- Multimodal neuroimaging and XR technologies offer real-time, high-resolution data on gait and brain activity.
- Integration of these technologies enhances understanding of brain-musculoskeletal coordination during walking.
- Challenges include data fusion and scalability, but AI could help translate these methods into clinical practice.

## Abstract

The analysis of human gait is a cornerstone in diagnosing and monitoring a variety of neuromuscular and orthopedic conditions. Recent technological advancements have paved the way for innovative methodologies that combine multimodal neuroimaging and eXtended Reality (XR) technologies to enhance the precision and applicability of gait analysis. This review explores the state-of-the-art solutions of an advanced gait analysis approach, a multidisciplinary concept that integrates neuroimaging, extended reality technologies, and sensor-based methods to study human locomotion. Several wearable neuroimaging modalities such as functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), commonly used to monitor and analyze brain activity during walking and to explore the neural mechanisms underlying motor control, balance, and gait adaptation, were considered. XR technologies, including virtual, augmented, and mixed reality, enable the creation of immersive environments for gait analysis, real-time simulation, and movement visualization, facilitating a comprehensive assessment of locomotion and its neural and biomechanical dynamics. This advanced gait analysis approach enhances the understanding of gait by examining both cerebral and biomechanical aspects, offering insights into brain–musculoskeletal coordination. We highlight its potential to provide real-time, high-resolution data and immersive visualization, facilitating improved clinical decision-making and rehabilitation strategies. Additionally, we address the challenges of integrating these technologies, such as data fusion, computational demands, and scalability. The review concludes by proposing future research directions that leverage artificial intelligence to further optimize multimodal imaging and XR applications in gait analysis, ultimately driving their translation from laboratory settings to clinical practice. This synthesis underscores the transformative potential of these approaches for personalized medicine and patient outcomes.