A new framework for self-adaptive artificial intelligence to personalize assistance for patients using robotic exoskeletons and prostheses

项目摘要 机器人假体和外骨骼可以通过适应对患者进行个性化的帮助，对 行动不便的人，例如截肢或中风的人。研究表明，流动性与 生活质量、参与和抑郁，这些技术具有显著的能力来增强人类的行走能力， 降低跌倒风险，提高整体生活质量。这项拟议的研究旨在创造可穿戴设备的范式转变 机器人领域通过创新一种新的人工智能(AI)框架来自适应机器人控制以实现个性化 帮助患者形成独特的行走方式。 这项工作的总体假设是，人工智能系统能够在动态的、非结构化的过程中进行自适应控制 社区步行可以改善使用机器人假体和外骨骼的患者的活动能力。 为了在智能可穿戴机器人应用中实现足够程度的适应，例如机器人外骨骼和 假肢，团队必须克服关键的科学差距：挑战1)确定用户意图的预测算法是 要么依赖于用户，需要大量的训练数据，要么独立于用户，具有高错误率。挑战2) 由于对代谢指标的依赖，目前调整控制策略的人在回路方法很慢，而且无法 优化静态环境之外的可穿戴机器人控制，例如固定速度的跑步机行走。 这些技术差距阻碍了将这种系统从实验室环境转化为现实世界社区使用的过程。 这项新的创新者计划将通过两项主要创新来解决这些差距：创新1)创建自适应 意图识别系统，学习单个患者的步态模式；创新2)制定人在循环中 (HIL)演员-评论家框架，最大化多目标奖励函数，以在用户和 环境状况。可穿戴机器人控制器框架的概念，可自适应意图识别 在移动任务中适应患者步态的系统和控制策略是新颖的，这在以前是前所未有的 调查过了。这些创新将首先在身体健全的控制对象中使用最先进的机器人进行验证 外骨骼技术是在PI的实验室开发的。创新一：S构想的自适应意图识别系统将 移植到机器人膝关节/脚踝假体平台上，并在经股截肢患者身上进行临床测试。 创新2 S概念演员关键网络自适应控制政策将转化为髋关节外骨骼 中风后的个体，并在临床实验中得到验证。患者与部署到可穿戴设备的人工智能系统的互动 机器人技术是加速这一领域的关键，不能从离线研究或健全的控制测试中得出结论。 最终，这些成果将使自适应可穿戴机器人技术能够通过以下方式改善患者的移动性 个性化的帮助。人工智能技术与可穿戴机器人技术相结合，有可能提高行走速度， 提高步态质量和稳定性，并使患者群体能够更容易地执行各种运动任务 机动性不足。这一功能承诺将转化为改善社区步行和提高生活质量。

Project Abstract Robotic prostheses and exoskeletons that can personalize assistance to a patient through adaptation are of great value for individuals with mobility challenges, such as those with amputation or stroke. Studies show mobility is strongly linked to quality of life, participation and depression, and these technologies have significant ability to enhance human ambulation, reduce fall risk, and improve overall quality of life. The proposed research aims to create a paradigm shift in the wearable robotics field by innovating a new artificial intelligence (AI) framework for self-adapting robotic control to personalize assistance to a patient’s unique walking pattern. The overall hypothesis of this work is that AI systems capable of self-adaptive control during dynamic, unstructured community ambulation can improve mobility in patients using robotic prostheses and exoskeletons. To enable sufficient levels of adaptation in intelligent wearable robotic applications, such as robotic exoskeletons and prostheses, the team must overcome critical scientific gaps: Challenge 1) Predictive algorithms to determine user intent are either user-dependent with significant training data requirements or user-independent with high error rates. Challenge 2) Current human-in-the-loop approaches to adapt control policy are slow due to reliance on metabolic measures and are unable to optimize wearable robotic control outside of a static environment, such as fixed-speed treadmill walking. These technological gaps have impeded the translation of such systems beyond lab settings to real-world community use. This New Innovator proposal will address these gaps through two primary innovations: Innovation 1) Create self-adaptive intent recognition systems that learn an individual patient’s gait patterns; Innovation 2) Formulate a human-in-the-loop (HIL) actor-critic framework that maximizes a multi-objective reward function to self-adapt control policy across users and environmental states. The concept of a controller framework for wearable robotics that self-adapts both an intent recognition system and control policy to accommodate patient gait across locomotion tasks is novel and has not been previously investigated. These innovations will initially be validated in able-bodied control subjects using state-of-the-art robotic exoskeleton technology developed in the PI’s lab. Innovation 1’s concepts of a self-adaptive intent recognition system will be translated to a robotic knee/ankle prosthesis platform and clinically tested on patients with transfemoral amputation. Innovation 2’s concepts of actor critical networks for self-adapting control policy will be translated to a hip exoskeleton for individuals post stroke and validated in clinical experiments. Patient interactions with AI systems deployed to wearable robotics are critical to accelerate the field and cannot be derived from offline studies or able-bodied control testing. Ultimately, the outcomes will enable self-adaptive wearable robotic technology to improve patient mobility through personalized assistance. AI technology combined with wearable robotics has the potential to increase walking speed, improve gait quality and stability, and enable more accessibility to diverse locomotion tasks for patient populations with mobility deficits. This functionality promises to translate to improved community ambulation and enhanced quality of life.