CHS: Medium: Collaborative Research: Electromyography (EMG)-Based Assistive Human-Machine Interface Design: Cognitive Workload and Motor Skill Learning Assessment

合作研究：基于肌电图（EMG）的辅助人机界面设计：认知负荷和运动技能学习评估在美国有超过10万人患有上肢截肢。这种截肢通常伴有严重的残疾。日常生活活动可能不再可能或需要额外的努力和时间。为了尽可能完全地恢复功能，截肢患者使用基于肌电图（EMG）的人机界面（hmi）控制的上肢假肢。然而，超过50%的假肢使用者报告由于使用挫折而对设备不满意和放弃。为了提高基于肌电图的辅助人机界面的可及性和实用性，研究与使用这些系统支持运动技能康复和日常生活活动相关的认知负荷是至关重要的。此外，由于个体肌肉组成和控制的差异，有必要针对特定患者的情况定制基于肌电图的辅助人机界面。然而，目前还没有关于哪些界面控制功能或多或少有助于支持精神运动任务的学习和表现的通用指导。通过在经过验证的运动技能学习模拟以及高要求的实时任务（特别是模拟驾驶）中测试各种控制界面原型，研究人员试图为工程师开发各种应用的基于肌电图的辅助HMI技术提供设计指导。该项目还为年轻的工业、计算机科学和生物医学工程专业学生提供辅助技术设计和开发方面的教育和培训。该项目的技术目标分为三个重点，包括(1)使用计算认知性能模型和机器学习算法确定基于肌电图的人机界面的认知负荷成本——开发的认知负荷模型将用于预测控制界面设计变化的学习潜力和性能结果；(2)通过将基于肌电图的人机界面与虚拟现实（VR）模拟相结合，展示基本的运动技能训练。特别是，研究人员对评估使用基于肌电图的人机界面的认知负荷差异是否转化为学习潜力和精神运动任务技能保留的差异感兴趣。(3)通过在高保真驾驶模拟器中演示基于肌电图的人机界面操作驾驶控制的可能性，将VR精神运动测试中训练的基本运动组件转化为现实世界的应用。这一重点为使用基于肌电图的人机界面的认知负荷差异的研究结果提供了经验验证，以及提高学习和操作任务性能的潜力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

CHS: Medium: Collaborative Research: Electromyography (EMG)-based Assistive Human-Machine Interface Design: Cognitive Workload and Motor Skill Learning AssessmentOver 100,000 people in the United States have upper limb amputations. Such amputations are usually associated with substantial disabilities. Activities of daily living may no longer be possible or require additional effort and time. To restore functional ability as fully as possible, amputee patients use upper-limb prostheses controlled by Electromyography (EMG)-based human-machine interfaces (HMIs). However, over 50% of prosthetic users report device dissatisfaction and abandonment due to frustration in use. In order to increase accessibility and utility of EMG-based assistive HMIs, it is critical to investigate the cognitive workload associated with using these systems for supporting motor skill rehabilitation and activities of daily living. In addition, with differences in individual muscle make-up and control, it is necessary to customize EMG-based assistive HMIs to particular patient conditions. However, at this time, there exists little to no general guidance on what interface control features may be more or less conducive to supporting learning and performance of psychomotor tasks. By testing various control interface prototypes in a validated motor skill learning simulation as well as high-demand, real-time tasks, specifically simulated driving, the investigators seek to provide design guidance for engineers to develop EMG-based assistive HMI technologies for various applications. This project also provides education and training for young industrial, computer science, and biomedical engineering students in assistive technology design and development.The technical aims of the project are divided into three thrusts including (1) identifying cognitive load costs of EMG-based HMIs using computational cognitive performance models and machine learning algorithms - The developed cognitive workload models will be used for predicting learning potential and performance outcomes of variations of control interface designs, (2) demonstrating fundamental motor skill training through integration of the EMG-based HMI with virtual reality (VR) simulations - In particular, the investigators are interested in assessing whether the difference in cognitive load of using EMG-based HMIs translates to variations in learning potential and retention of psychomotor task skills, (3) translating fundamental motion components trained in the VR psychomotor test to a real-world application by demonstrating the possibility of operational driving control with an EMG-based HMI in a high-fidelity driving simulator - This thrust provides empirical validation for the findings regarding differences in cognitive load of using EMG-based HMIs, as well as the potential for increased learning and operational task performance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.