Citation

Sokołowska B, Świderski W, Smolis-Bąk E, Sokołowska E, Sadura-Sieklucka T. Front. Comput. Neurosci. 2024; 18: e1390208.

Copyright

(Copyright © 2024, Frontiers Research Foundation)

DOI

10.3389/fncom.2024.1390208

PMID

38808222

PMCID

PMC11130377

Abstract

INTRODUCTION: Novel technologies based on virtual reality (VR) are creating attractive virtual environments with high ecological value, used both in basic/clinical neuroscience and modern medical practice. The study aimed to evaluate the effects of VR-based training in an elderly population.

MATERIALS AND METHODS: The study included 36 women over the age of 60, who were randomly divided into two groups subjected to balance-strength and balance-cognitive training. The research applied both conventional clinical tests, such as (a) the Timed Up and Go test, (b) the five-times sit-to-stand test, and (c) the posturographic exam with the Romberg test with eyes open and closed. Training in both groups was conducted for 10 sessions and embraced exercises on a bicycle ergometer and exercises using non-immersive VR created by the ActivLife platform. Machine learning methods with a k-nearest neighbors classifier, which are very effective and popular, were proposed to statistically evaluate the differences in training effects in the two groups.

RESULTS AND CONCLUSION: The study showed that training using VR brought beneficial improvement in clinical tests and changes in the pattern of posturographic trajectories were observed. An important finding of the research was a statistically significant reduction in the risk of falls in the study population. The use of virtual environments in exercise/training has great potential in promoting healthy aging and preventing balance loss and falls among seniors.

Language: en

Keywords

aging; virtual reality; machine learning; fall risk; balance and cognitive training; body balance; k-NN algorithm; postural stability