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

自适应人工智能的新框架，可为使用机器人外骨骼和假肢的患者提供个性化帮助

• 批准号: 10472098
• 负责人: Aaron John Young
• 金额: $ 142.38万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-19 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10472098
• 关键词: Address; Amputation; Artificial Intelligence; Clinical; Communities; Data; Environment; Expert Systems; Gait; Hip region structure; Human; Individual; Intelligence; Knee; Learning; Link; Locomotion; Measures; Mental Depression; Metabolic; Outcome; Patients; Pattern; Policies; Prosthesis; Quality of life; Research; Rewards; Robotics; Speed; Stroke; System; Technology; Testing; Training; Translating; Translations; Walking; Work; ankle prosthesis; exoskeleton; experimental study; fall risk; human-in-the-loop; improved; improved mobility; individual patient; innovation; novel; patient assistance; patient mobility; patient population; post stroke; prediction algorithm; public health research; robot control; robot exoskeleton; treadmill; walking speed

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.

项目摘要 可以通过适应来个性化对患者的帮助的机器人假体和外骨骼对于患者具有很大的价值。 行动不便的人，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，这些技术有显着的能力，以提高人类的amperity， 降低跌倒风险，提高整体生活质量。拟议的研究旨在创造一个可穿戴设备的范式转变， 通过创新新的人工智能（AI）框架，自适应机器人控制， 帮助患者的独特行走模式。 这项工作的总体假设是，人工智能系统能够在动态，非结构化的过程中进行自适应控制。 社区假肢可以使用机器人假肢和外骨骼改善患者的活动能力。 为了在智能可穿戴机器人应用中实现足够的适应水平，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 或者依赖于用户，具有显著的训练数据要求，或者不依赖于用户，具有高错误率。挑战2） 目前的人在回路方法，以适应控制政策是缓慢的，由于依赖于代谢措施， 在静态环境之外优化可穿戴机器人控制，例如固定速度的跑步机行走。 这些技术差距阻碍了将这些系统从实验室环境转换到现实世界的社区使用。 新的创新者提案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，学习个体患者的步态模式;创新2）制定人在环 (HIL)最大化多目标奖励函数以跨用户自适应控制策略的行动者-批评者框架， 环境国家。可穿戴机器人控制器框架的概念，自适应意图识别 在运动任务中适应患者步态系统和控制策略是新颖的， 研究了这些创新将首先在健全的控制对象中使用最先进的机器人进行验证。 PI实验室研发的外骨骼技术创新1的自适应意图识别系统的概念将 将其转化为机器人膝关节/踝关节假体平台，并在经股截肢患者身上进行临床测试。 创新2的用于自适应控制策略的演员关键网络的概念将被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。患者与部署在可穿戴设备上的AI系统的交互 机器人技术对于加速该领域的发展至关重要，不能从离线研究或健全的控制测试中获得。 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，有可能提高步行速度， 改善步态质量和稳定性，并使患者群体更容易完成各种运动任务， 流动性不足。这一功能有望转化为改善社区环境和提高生活质量。