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

CHS：Medium：协作研究：基于肌电(EMG)的辅助人机界面设计：认知工作量和运动技能学习评估美国有超过10万人接受上肢截肢手术。这样的截肢通常与严重的残疾有关。日常生活活动可能不再可能，或需要额外的努力和时间。为了尽可能地恢复功能，截肢患者使用基于肌电(EMG)的人机界面(HMI)控制的上肢假体。然而，超过50%的义肢使用者报告说，由于在使用过程中感到沮丧，义肢使用者对义肢不满意或被遗弃。为了增加基于肌电的辅助HMI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工作量是至关重要的。此外，由于个体肌肉组成和控制的不同，有必要根据特定的患者情况定制基于EMG的辅助HMI。然而，目前，对于哪些界面控制功能可能或多或少有助于支持精神运动任务的学习和执行，几乎没有普遍的指导意见。通过在有效的运动技能学习模拟中测试各种控制接口原型，以及高要求的实时任务，特别是模拟驾驶，调查人员试图为工程师提供设计指导，以便为各种应用开发基于肌电的辅助人机界面技术。这个项目还为年轻的工业、计算机科学和生物医学工程专业的学生提供辅助技术设计和开发方面的教育和培训。项目的技术目标分为三个方面，包括(1)使用计算认知性能模型和机器学习算法确定基于EMG的HMI的认知负载成本-所开发的认知负载模型将用于预测各种控制界面设计的学习潜力和性能结果，(2)通过将基于EMG的HMI与虚拟现实(VR)模拟相结合来演示基本运动技能培训-特别是，研究人员感兴趣的是评估使用基于EMG的HMI的认知负荷的差异是否会转化为学习潜力和保持精神运动任务技能的变化，(3)通过在高保真驾驶模拟器中演示使用基于EMG的HMI进行操作驾驶控制的可能性，将在VR心理运动测试中训练的基本运动成分转化为真实世界的应用--这一推动力为关于使用基于EMG的HMI的认知负荷差异的发现提供了经验验证，这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.