Neuroergonomic Assessment of Wheelchair Control Using Mobile fNIRS.
Neuroergonomic Assessment of Wheelchair Control Using Mobile fNIRS.
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DOI：
10.1109/tnsre.2020.2992382
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Ayaz H
中科院分区：
文献类型：
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作者：
Joshi S;Herrera RR;Springett DN;Weedon BD;Ramirez DZM;Holloway C;Dawes H;Ayaz H
两个多世纪以来，轮椅一直是运动障碍者最常见的辅助设备之一，没有经过太多修改。轮椅控制是一项复杂的运动任务，增加了身体和认知的工作量。新的轮椅接口，包括动力辅助设备，可以通过减少所需的体力来进一步增强用户，但对脑力的影响知之甚少。在这项研究中，我们采用了神经人体工程学的方法，利用移动的和无线功能近红外光谱（fNIRS）为基础的大脑监测身体活动的参与者。48名志愿者（30名新手和18名经验丰富的）在简单和复杂的现实环境中使用和不使用PowerAssist界面的轮椅自行行走。结果表明，正如预期的那样，与简单环境相比，复杂的更困难的环境导致更低的任务绩效，辅之以更高的前额叶皮层活动。与仅适用于新手的传统手动控制相比，使用PowerAssist功能的大脑激活显着降低。专业知识导致额中回的大脑激活模式较低，辅之以涉及较低认知工作量的性能指标。这里的结果证实了神经工效学方法的潜力，直接的神经活动措施可以补充和提高任务性能指标。我们的结论是，认知工作负荷的PowerAssist的好处更多地针对新用户和困难的设置。这里展示的方法可以在未来的研究中使用，以实现更大的个性化和理解的移动接口在现实世界中的动态环境。
For over two centuries, the wheelchair has been one of the most common assistive devices for individuals with locomotor impairments without many modifications. Wheelchair control is a complex motor task that increases both the physical and cognitive workload. New wheelchair interfaces, including Power Assisted devices, can further augment users by reducing the required physical effort, however little is known on the mental effort implications. In this study, we adopted a neuroergonomic approach utilizing mobile and wireless functional near infrared spectroscopy (fNIRS) based brain monitoring of physically active participants. 48 volunteers (30 novice and 18 experienced) self-propelled on a wheelchair with and without a PowerAssist interface in both simple and complex realistic environments. Results indicated that as expected, the complex more difficult environment led to lower task performance complemented by higher prefrontal cortex activity compared to the simple environment. The use of the PowerAssist feature had significantly lower brain activation compared to traditional manual control only for novices. Expertise led to a lower brain activation pattern within the middle frontal gyrus, complemented by performance metrics that involve lower cognitive workload. Results here confirm the potential of the Neuroergonomic approach and that direct neural activity measures can complement and enhance task performance metrics. We conclude that the cognitive workload benefits of PowerAssist are more directed to new users and difficult settings. The approach demonstrated here can be utilized in future studies to enable greater personalization and understanding of mobility interfaces within real-world dynamic environments.