利用时间步态特征和机器学习方法预测跌倒风险。

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Artificial Intelligence Pub Date : 2024-08-28 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1425713
Zhe Khae Lim, Tee Connie, Michael Kah Ong Goh, Nor 'Izzati Binti Saedon
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引用次数: 0

摘要

引言跌倒已被公认为是全世界的一个重大公共卫生问题。及早发现跌倒风险对采取预防措施至关重要。传统的临床评估虽然可靠,但需要耗费大量资源,而且并不总是可行:本研究通过计算机视觉和机器学习技术,利用步态分析,探索人工智能(AI)在预测跌倒风险方面的功效。数据收集采用了MMU合作者提供的定时起立行走(TUG)测试和JHFRAT评估,并利用Mendeley提供的涉及老年人的公共数据集进行了扩充。研究介绍了一种提取和分析步态特征(如步幅时间、步幅时间、步频和站立时间)的稳健方法,以区分跌倒者和非跌倒者:研究了两种实验设置:一种考虑了每只脚的单独步态特征,另一种分析了两只脚的平均特征。最终,提出的解决方案取得了可喜的成果,大大提高了模型实现高准确度的能力。其中,LightGBM 在预测任务中的准确率高达 96%:讨论:研究结果表明,简单的机器学习模型可以根据步态特征成功识别出跌倒风险较高的个体,其结果很有可能简化跌倒风险评估流程。然而,在整个实验过程中也发现了一些局限性,包括数据集不足和数据变化,从而限制了模型的通用性。这些问题都需要在今后的工作中加以考虑。总之,这项研究为不断增长的跌倒风险预测知识做出了贡献,并强调了人工智能通过早期识别高危人群来加强公共卫生策略的潜力。
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Fall risk prediction using temporal gait features and machine learning approaches.

Introduction: Falls have been acknowledged as a major public health issue around the world. Early detection of fall risk is pivotal for preventive measures. Traditional clinical assessments, although reliable, are resource-intensive and may not always be feasible.

Methods: This study explores the efficacy of artificial intelligence (AI) in predicting fall risk, leveraging gait analysis through computer vision and machine learning techniques. Data was collected using the Timed Up and Go (TUG) test and JHFRAT assessment from MMU collaborators and augmented with a public dataset from Mendeley involving older adults. The study introduces a robust approach for extracting and analyzing gait features, such as stride time, step time, cadence, and stance time, to distinguish between fallers and non-fallers.

Results: Two experimental setups were investigated: one considering separate gait features for each foot and another analyzing averaged features for both feet. Ultimately, the proposed solutions produce promising outcomes, greatly enhancing the model's ability to achieve high levels of accuracy. In particular, the LightGBM demonstrates a superior accuracy of 96% in the prediction task.

Discussion: The findings demonstrate that simple machine learning models can successfully identify individuals at higher fall risk based on gait characteristics, with promising results that could potentially streamline fall risk assessment processes. However, several limitations were discovered throughout the experiment, including an insufficient dataset and data variation, limiting the model's generalizability. These issues are raised for future work consideration. Overall, this research contributes to the growing body of knowledge on fall risk prediction and underscores the potential of AI in enhancing public health strategies through the early identification of at-risk individuals.

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来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
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