Collaborative Research: Predicting and Optimizing User Comfort for Lower-limb Exoskeletons through Mutual Motor Adaptations

辅助机器人设备，如外骨骼，可用于增强人类能力，帮助体力劳动，并改善康复。用户感知的舒适度对于辅助机器人设备的广泛采用至关重要，但舒适度的定义和测量仍然难以捉摸。该项目的研究目标是系统地定义，建模和优化人类用户在使用动力腿外骨骼行走时所感受到的舒适度。 该项目追求三个目标：第一个是开发一个基于下肢外骨骼机器人行走过程中记录的生物信号的舒适度模型。第二个优化参数的外骨骼控制的基础上，口头报告的用户舒适度。第三个优化了外骨骼控制的舒适性，而无需直接的用户报告。如果成功，该项目将导致新一代的外骨骼设备，更适合他们设计服务的人类。这将使大量有步态障碍的人受益，从而促进国民健康和福利。 这项工作的更广泛的影响，包括新的，动手外展活动，为代表性不足的少数民族在宾夕法尼亚州中部和达拉斯，得克萨斯州。该项目采取了重大步骤，对机器人控制器的发展，不断适应每个用户，并尽量减少用户的不适。这将通过使用神经网络模型来分析和建模用户的生物信号的选定集合（例如，代谢成本、心率、肌肉激活、运动学和动力学）来开发一种新的舒适度预测器，然后通过创建智能控制器，通过人在回路强化学习来最大化用户舒适度。人体受试者实验计划使用两个设备（膝盖和髋关节设备，和脚踝设备），以验证舒适度预测器可以有效地使用优化方法。 如果成功的话，该项目将使大量的步态障碍患者和需要机器人辅助的体力劳动者受益。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Assistive robotic devices such as exoskeletons can be used to enhance human capabilities, help with physically-intense labor, and improve rehabilitation. User-perceived comfort is critically important for the wide-spread adoption of assistive robotic devices, yet the definition and measurement of comfort remains elusive. The research objective of this project is to systematically define, model, and optimize the comfort perceived by human users while walking with powered leg exoskeletons. The project pursues three objectives: The first develops a model of comfort based on biosignals recorded during walking with lower limb exoskeleton robots. The second optimizes parameters of exoskeleton control based on verbalized reports of user comfort. The third optimizes exoskeleton control for comfort without direct user reporting. If successful, the project will lead to a new generation of exoskeleton devices that are more compatible with the humans they are designed to serve. This would benefit a large population of people with gait impairments, thereby advancing the national health and welfare. Broader impacts of this work include novel, hands-on outreach activities serving underrepresented minorities in central Pennsylvania and in Dallas, Texas.This project takes significant steps towards the development of robotic controllers that adapt continuously to each user and minimize user discomfort. This will be achieved by using neural network models to analyze and model a selected set of the users' biological signals (e.g., metabolic cost, heart rate, muscle activation, kinematics, and kinetics) to develop a novel comfort predictor, and then by creating intelligent controllers that maximize the user comfort via human-in-the-loop reinforcement learning. Human subject experiments are planned using two devices (a knee and hip device, and an ankle device) to verify that the comfort predictor can be used effectively with the optimization method. If successful, the project could benefit a large population of people with gait impairments and those requiring robotic assistance with physically-intense labor.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.