Collaborative Research: An Integrated, Proactive, and Ubiquitous Prosthetic Care Robot for People with Lower Limb Amputation: Sensing, Device Designing, and Control

下肢截肢的个体在行走时面临许多缺点，例如下背部的压力增加，代谢成本增加和步态不对称。现有的假肢机器人处方有几个不足之处：它们要么不能提供准确和及时的设备控制，要么只能在受限的实验室环境中使用。下肢截肢者面临的关键挑战是没有视觉信息对设备的反馈控制。因此，身体的其他部分需要额外的肌肉组织招募来弥补增加的不稳定性，从而增加了跌倒和退行性关节问题的风险。该奖项旨在开发一种新型的假肢机器人，该机器人将使用来自用户视觉信息的神经肌肉反馈重建，以在不同的行走条件下提供主动和最佳控制。该项目的成果将有助于临床医生在开具下肢假肢处方时提供更好的护理，并对生活在美国的截肢者的生活产生积极影响。此外，该项目还将为工程、计算机科学和医学专业的本科生和研究生创建一个多学科教育计划，包括设计和控制知识、人机界面、传感器和机器学习。残疾学生也将通过数据收集、参与会议和指导等方式参与。本项目的目标是开发一种新的假肢机器人，该机器人能够进行主动和用户特定的假肢控制，以改善各种日常生活条件下的行走功能。这种方法依赖于传感策略、设备硬件和控制框架的相互关联的进步。该研究包括：1）开发一种仅使用可穿戴惯性运动单元（IMU）传感器的运动捕捉方法，以重建和预测日常生活中的人体步态，同时实现实验室之外的评估和临床诊断。这将通过知识蒸馏来实现，该知识蒸馏利用使用多种传感模式的复杂教师网络来训练使用IMU传感器的更简单的学生网络。2)开发一种轻量化、节能、半主动的气动和液压混合装置，使用户能够根据操作环境调整设置。3)开发一种基于自适应学习的自适应算法来控制和同步用户-设备协作。此外，将从实时系统领域借鉴混合临界设计范式，以确保机器人的安全性和稳定性。本项目得到跨部门机器人基础研究计划的支持，由工程局（ENG）和计算机与信息科学与工程局（CISE）共同管理和资助该奖项反映了NSF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。

Individuals with lower limb amputation face many disadvantages while walking, such as increased strains on the lower back, increased metabolic cost, and gait asymmetry. Existing prosthetic robot prescriptions suffer from several deficiencies: they either cannot provide accurate and prompt device control or can only be used in a constrained laboratory environment. The key challenge for lower limb amputees is that there is no feedback control to the device from the visual information. As a result, the rest of the body requires extra musculature recruitment to compensate for increased instability, posing a greater risk of falling and degenerative joint issues. This award aims to develop a novel prosthetic robot that would use reconstruction of the neuromuscular feedback from user visual information to provide proactive and optimal control under different walking conditions. The outcome of this project will help clinicians provide enhanced care when prescribing lower limb prostheses and positively affect the lives of amputees living within the United States. In addition, this project will create a multidisciplinary education program comprising design and control knowledge, human-machine interface, sensors, and machine learning for undergraduate and graduate students from engineering, computer science, and medicine. Students with disabilities will also participate via data collection, involvement in meetings, and mentorship.The objective of this project is to develop a new prosthetic robot that enables proactive and user-specific prosthetic control to improve walking function in a variety of daily life conditions. The approach relies on interrelated advances in sensing strategy, device hardware, and control framework. The research incorporates: 1) Developing a motion capture method using only wearable inertial motion unit (IMU) sensors to reconstruct and predict human gait during daily life, while enabling assessment and clinical diagnosis beyond lab settings. This will be achieved through knowledge distillation that leverages a complex teacher network using multiple sensing modalities to train a simpler student network that uses IMU sensors. 2) Developing a lightweight, energy-efficient, and semi-active pneumatic and hydraulic hybrid device to enable the user to tune settings as per the operating environment. 3) developing a reinforcement-learning-based adaptive algorithm to control and sync user-device cooperation. In addition, the mixed-criticality design paradigm will be borrowed from real-time systems field to ensure the robot safety and stability.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.