# Assessment of Fall Risk in Neurological Disorders and Technology: Relationship Between Silver Index and Gait Analysis

## TL;DR

This study explores how gait analysis and the Silver Index can help assess fall risk in stroke patients, but finds limited relevance for Parkinson's and Multiple Sclerosis.

## Contribution

The study identifies a correlation between the Silver Index and gait parameters in stroke patients, suggesting its potential for fall risk assessment.

## Key findings

- The Silver Index correlates with spatiotemporal gait parameters in stroke patients, indicating potential for fall risk assessment.
- No significant correlations were found between the Silver Index and gait parameters in Parkinson's and Multiple Sclerosis patients.
- Gait kinematic parameters showed no significant relationship with the Silver Index in any patient group.

## Abstract

Falls are one of the most common and devastating effects of neurological diseases, especially in patients with stroke outcomes, Parkinson’s Disease (PD), and Multiple Sclerosis (MS). To prevent negative outcomes and guide tailored rehabilitation, it is necessary to identify risk factors early. The current study aims to assess whether and how the risk of falling is related to spatiotemporal and kinematic parameters in stroke, PD, and MS. It also seeks to determine how these factors can help manage patients and identify more personalized and appropriate rehabilitation treatments. Ninety patients with neurological disorders (stroke, PD, and MS) underwent eight weeks of home-based rehabilitation using the ARC Intellicare device or following a paper-based protocol. At baseline (T0) and at the end of the protocol (T2), they were assessed using the Silver Index of the hunova® robotic platform to evaluate fall risk, and instrumental gait analysis to record spatiotemporal and kinematic parameters of walking. Statistical analysis showed moderate and significant correlations between the Silver Index and gait spatiotemporal parameters such as stance and swing phase, both in affected (T0, p = 0.007; T2, p = 0.017) and unaffected side (T0, p = 0.022; T2, p = 0.008), double support in affected side (T0, p = 0.002; T2, p = 0.005), cycle length in affected (T0, p = 0.007; T2, p = 0.003) and unaffected side (T0, p = 0.008; T2, p = 0.003), and cadence (T0, p = 0.025; T2, p = 0.003) in stroke patients. No significant results emerged in the PD and MS. No population showed significant correlations between the Silver Index and gait kinematic parameters. The Silver Index may reflect distinct patterns of instability in post-stroke gait, but in PD and MS, multiple factors influence the risk of falling that instrumental gait analysis cannot fully capture, requiring a more extensive and multidimensional approach that includes cognitive aspects.