Intelligent Human-Interactive, Assistive, and Rehabilitation Systems and Robots

2017年加拿大残疾调查显示，五分之一的加拿大人（620万人）在生活中经历过一种或多种残疾，限制了他们的灵活性、移动性、灵活性、视力或认知能力。最近在计算、通信、机器人和人工智能方面的进步为更智能的康复或辅助设备创造了新的机会，这些设备可以更快地恢复或更好地执行日常任务。随着需要更密切的人机交互或合作的新应用的出现，出现了一些关于设计、智能、控制、安全性、可靠性、适应性、有效性和人为因素的研究问题。卡尔顿大学的先进生物机电和运动实验室（ABL）的目标是开发和验证智能机器来解决这些新的研究问题。本提案建立在过去的实验康复和辅助机器人研究的基础上，并通过引入和研究新的智能控制和传感策略来加强这一领域，以确保更安全，更智能，适应性强，可靠的物理人机交互结构如下：该项目的具体目标包括：引入和验证新型智能学习辅助设备（iLEAD）框架，该框架使一类辅助设备能够适应其人类用户的行为/状态变化。-推进力控制方法，保证人机交互的稳定性。-推进基于光纤的生物力学柔性传感器和反馈控制回路，包括柔性肢体传感（CLS）概念。-使用拟议的控制器推进我们现有的康复和辅助设备，并与当地医疗机构合作进行最终用户验证实验。这项研究可以应用于辅助设备与人类交互的众多应用。该结果对医疗设备和工程界具有重要意义，并可能影响行动不便或其他类型障碍的加拿大人的生活。

The 2017 Canadian Survey on Disability reveals that one in five Canadians (6.2 million) have experienced one or more disabilities in their lives limiting their abilities such as dexterity, mobility, flexibility, vision, or cognition. Recent advancements in computing, communication, robotics, and AI have created new opportunities for more intelligent rehabilitation or assistive devices enabling faster recovery or better performing daily tasks. With the emergence of novel applications requiring more intimate human-machine interaction or cooperation, several research questions arise regarding the design, intelligence, controls, safety, reliability, adaptability, efficacy, and human factors that need to be addressed. The Advanced Biomechatronics and Locomotion Laboratory (ABL) at Carleton University aim is to develop and validate intelligent machines to address such new research questions. This proposal builds upon the past research on experimental rehabilitation and assistive robotics and enhances the area by introducing and investigating new classes of intelligent control and sensing strategies to ensure safer, more intelligent, adaptable, and reliable physical human-machine interactions structured as follows: The specific goals of the project include:  -Introduction and validation of the novel Intelligent Learning Assistive Devices (iLEAD) framework that enable a class of assistive devices to adapt to their human users' behaviour/state variations. -Advancing force control methodologies with guaranteed stability for human machine interactions. -Advancing fibre optic based biomechanical compliant sensors and feedback control loops including compliant limb sensing (CLS) concept. -Advancing our current rehabilitation and assistive devices using the proposed controllers and conduct end-user validation experiments in collaboration with local healthcare organizations. This research can be applied to multitude of applications where an assistive device is interacting with a human. The outcome is of importance to medical device and engineering community and could impact the life of Canadians with mobility or other types of impairments.