{Reference Type}: Journal Article
{Title}: A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women.
{Author}: Sokołowska B;Świderski W;Smolis-Bąk E;Sokołowska E;Sadura-Sieklucka T;
{Journal}: Front Comput Neurosci
{Volume}: 18
{Issue}: 0
{Year}: 2024
{Factor}: 3.387
{DOI}: 10.3389/fncom.2024.1390208
{Abstract}: <B>UNASSIGNED: </B>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.<BR><B>UNASSIGNED: </B>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.<BR><B>UNASSIGNED: </B>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.