采用机器学习方法评估虚拟平衡/认知训练对老年妇女跌倒风险的影响

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Beata Sokołowska, Wiktor Świderski, Edyta Smolis-Bąk, Ewa Sokołowska, Teresa Sadura-Sieklucka
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引用次数: 0

摘要

导言基于虚拟现实(VR)的新技术正在创造出具有高生态价值的迷人虚拟环境,这些技术在基础/临床神经科学和现代医疗实践中都得到了应用。该研究旨在评估基于虚拟现实技术的训练对老年人群的影响。研究对象包括 36 名 60 岁以上的女性,她们被随机分为两组,分别接受平衡-力量和平衡-认知训练。研究同时应用了传统的临床测试,如(a)定时起立行走测试;(b)五次坐立测试;以及(c)睁眼和闭眼后的罗伯格测试。两组的训练都进行了 10 次,包括在自行车测力计上进行的练习和使用 ActivLife 平台创建的非沉浸式 VR 进行的练习。研究提出了使用 k-nearest neighbors 分类器的机器学习方法,该方法非常有效且广受欢迎,用于统计评估两组训练效果的差异。研究的一个重要发现是,研究人群的跌倒风险在统计学上显著降低。在锻炼/训练中使用虚拟环境在促进健康老龄化、预防老年人平衡能力丧失和跌倒方面具有巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women
IntroductionNovel 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 methodsThe 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 conclusionThe 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.
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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