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

2017年加拿大残疾人调查显示，五分之一(620万)的加拿大人在生活中经历过一种或多种残疾，限制了他们的灵活性、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，使其能够更快地康复或更好地执行日常任务。随着需要更密切的人机交互或协作的新型应用的出现，出现了一些关于设计、智能、控制、安全性、可靠性、适应性、有效性和需要解决的人的因素的研究问题。卡尔顿大学的高级生物机电一体化和运动实验室(ABL)的目标是开发和验证智能机器，以解决这些新的研究问题。这项建议建立在过去对实验性康复和辅助机器人的研究基础上，并通过引入和研究新的智能控制和传感策略来加强该领域，以确保更安全、更智能、更适应性和可靠的物理人机交互，结构如下：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，使一类辅助设备能够适应人类用户的行为/状态变化。-进步力控制方法，保证人机交互的稳定性。-先进的基于光纤的生物力学顺应性传感器和反馈控制回路，包括顺应性肢体传感(CLS)概念。-使用建议的控制器改进我们目前的康复和辅助设备，并与当地医疗保健组织合作进行最终用户验证实验。这项研究可以应用于许多辅助设备与人类互动的应用中。这一结果对医疗器械和工程界具有重要意义，可能会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.