建筑中主动式背部支撑外骨骼的认知负荷评估:建筑框架案例研究

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Advanced Engineering Informatics Pub Date : 2024-10-01 DOI:10.1016/j.aei.2024.102905
Abiola Akanmu , Akinwale Okunola , Houtan Jebelli , Ashtarout Ammar , Adedeji Afolabi
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引用次数: 0

摘要

主动式背部支撑外骨骼已成为减轻建筑行业与工作有关的肌肉骨骼疾病的潜在解决方案。然而,研究揭示了与使用外骨骼相关的意外后果,其中最明显的是认知负荷的增加。研究表明,认知负荷的增加会耗尽工作记忆,从而可能妨碍任务执行和态势感知。尽管外骨骼使用者容易受到认知负荷增加的影响,但在执行建筑任务时对这种风险的实证评估却很有限。本研究评估了在执行建筑任务时使用主动式背部支撑外骨骼所带来的认知负荷。在使用和不使用主动式背部支撑外骨骼的情况下,都进行了使用脑电图捕捉大脑活动的实验。案例研究考虑了一项涉及六个子任务的建筑框架任务。通过脑电图信号的阿尔法波段来评估参与者在测试条件和子任务中的认知负荷。研究确定了使用外骨骼时评估认知负荷最敏感的脑电图通道。为了对收集到的数据进行推断,还进行了统计测试,包括单向重复测量方差分析、配对 t 检验和斯皮尔曼等级检验。结果表明,在执行木工框架任务时使用主动式背部支撑外骨骼会增加参与者的认知负荷,五个重要脑电图通道中的四个都表明了这一点。在评估认知负荷时,额叶和枕叶的选定通道成为最有影响力的通道。此外,该研究还探讨了脑电图通道之间的关系,揭示了额叶选定通道之间以及枕叶和额叶通道之间的强相关性。这些发现加深了人们对特定脑区在建筑任务中如何对使用主动式背部支撑外骨骼做出反应的理解。通过确定哪些脑区受到的影响最大,这项研究有助于优化外骨骼设计以更好地管理认知负荷,从而有可能提高这些设备在建筑环境中的人体工学效果和安全性。
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Cognitive load assessment of active back-support exoskeletons in construction: A case study on construction framing
Active back-support exoskeleton has emerged as a potential solution for mitigating work-related musculoskeletal disorders within the construction industry. Nevertheless, research has unveiled unintended consequences associated with its usage, most notably increased cognitive load. Elevated cognitive load has been shown to deplete working memory, potentially impeding task performance and situational awareness. Despite the susceptibility of exoskeleton users to increased cognitive load, there has been limited empirical evaluation of this risk while performing construction tasks. This study evaluates the cognitive load associated with using an active back-support exoskeleton while performing construction tasks. An experiment was conducted to capture brain activity using an Electroencephalogram, both with and without the use of an active back-support exoskeleton. A construction framing task involving six subtasks was considered as a case study. The participants’ cognitive load was assessed for the tested conditions and subtasks through the alpha band of the Electroencephalogram signals. The study identified the most sensitive Electroencephalogram channels for evaluating cognitive load when using exoskeletons. Statistical tests, including a one-way repeated measure ANOVA, paired t-test, and Spearman Rank were conducted to make inferences about the collected data. The results revealed that using an active back-support exoskeleton while performing the carpentry framing task increased the cognitive load of the participants, as indicated by four out of five significant Electroencephalogram channels. Selected channels in the frontal and occipital lobes emerged as the most influential channels in assessing cognitive load. Additionally, the study explores the relationships among Electroencephalogram channels, revealing strong correlations between selected channels in the frontal lobe and between channels in the occipital and frontal lobes. These findings enhance understanding of how specific brain regions respond to the use of active back support exoskeletons during construction tasks. By identifying which brain regions are most affected, this study contributes to optimizing exoskeleton designs to better manage cognitive load, potentially improving both the ergonomic effectiveness and safety of these devices in construction environments.
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来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
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