Intelligent Mobility Systems

随着我们的人口老龄化，保持身体的流动性对于独立、生活质量和适当的老龄化非常重要。需要技术和方法的融合，以进一步提高老年人和行动不便的人的行动方式。智能移动系统(IMS)的研究将实时信息、传感器辅助决策、运动控制和运动生成结合在一起，以在个人选择的环境中提供安全有效的移动。IMS的研究项目涉及两个相互关联的流：智能可穿戴辅助设备和实时运动评估。这两个流都需要从可穿戴传感器进行实时多信号采集、特征提取、分类和输出，以指导辅助设备功能或为临床决策提供即时信息。智能辅助设备流还需要机械工程创新，以实现可穿戴、耐用、成本合适的解决方案，这些解决方案可以应用于行动不便的人群。奥塔沃克速度液压膝关节概念将成为下一代膝关节控制的基础，为膝关节伸展能力有限或可以忽略的人在其社区步行(即楼梯、坡道、跌倒恢复)提供适当的可变减震。有效的微处理器-流体流动的控制应该提供适当的膝关节屈曲阻力，以确保安全运动，但在适当的情况下，为能量有效的步态提供最小的阻力。唯一可用的可变阻力膝关节踝足矫形器(Kafo)是配备在Kafo上的假膝单元。拟议的Ottawalk设备将进行创新，提供更轻、更低的外形和卓越的受控设备，以提高移动性。在奥特沃克矫形器研究的同时，将启动使用新的Exolegs下肢机器人外骨骼平台的研究。这一新的加拿大外骨骼方法为研究瘫痪或双侧下肢无力的人提供了一个可行的平台。作为一种自立式步行架，尺寸和重量的限制不那么严格。然而，最佳机械功能和控制问题仍然存在。为了改进生物力学决策工具，可穿戴传感器、可穿戴视频和智能手机技术将结合在一起，以更好地了解实验室外的人体运动。改善生物力学信息的即时性应该会促进量化和循证决策的采用，潜在地改善运动障碍患者的预后。需要研究可穿戴式和智能手机传感器的获取和定位、传感器融合以生成可用的结果、将结果与多媒体集成以进行增强现实演示，以及确定将量化结果整合到临床实践中的有效实时方法。这项研究将建立在我们最近的智能手机应用研究的基础上，以加强临床测试，提供身体位置的增强现实分析，并改进智能手机传感器和视频的活动分类。虽然已经开发了原型应用程序，但还需要进行试点测试来验证应用程序在实践中的性能。下一阶段的研究将改进病态步态(截肢者、中风等)患者基于智能手机的活动分类，增强对人体运动的实时/增强现实分析的视频工具，将新的智能手机传感器集成到分类算法中，并提供可在患者接触点使用的基于智能手机的即时人体运动分析。

As our population ages, maintaining physical mobility is important for independence, quality of life, and aging in place. A convergence of technologies and methods are needed to further enhance how the elderly and people with mobility deficits move. Intelligent Mobility Systems (IMS) research brings together real-time information, sensor-aided decision-making, movement control, and movement generation to provide safe and effective locomotion in a person’s chosen environment. The IMS research program involves two inter-related streams: intelligent-wearable assistive devices and real-time movement evaluation. Both streams require real-time multi-signal acquisition from wearable sensors, feature extraction, classification, and output to either guide assistive device function or provide immediate information for clinical decision-making. The intelligent assistive device stream also requires mechanical engineering innovations to achieve wearable, durable, cost appropriate solutions that can be applied across the mobility disabled population. The Ottawalk-speed hydraulic knee joint concept will be the basis for the next generation in knee joint control by providing appropriate variable dampening as a person with limited or negligible knee extensor capacity walks in their community (i.e., stairs, ramps, stumble recovery). Effective microprocessor-control of fluid flow should provide appropriate knee flexion resistance for safe movement but supply minimal resistance, when appropriate, for energy efficient gait. The only available variable resistance knee ankle foot orthosis (KAFO) is a prosthetic knee unit outfitted to a KAFO. The proposed Ottawalk device will innovate by providing a lighter, lower profile, and superior controlled device for improved mobility. In parallel with the Ottawalk orthosis research, research with the new Exolegs lower extremity robotic exoskeleton platform will be initiated. This new Canadian exoskeleton approach presents a viable platform for research on powered mobility for people with paralysis or bilateral lower extremity weakness. As a self-supporting walking frame, size and weight limitations are less severe. However, optimal mechanical function and control questions remain. To improve tools for biomechanical decision-making, wearable sensors, wearable video, and smartphone technologies will be combined to provide a better understanding of human movement outside the laboratory. Improving immediacy of biomechanical information should improve adoption of quantitative and evidence-based decision-making, potentially improving outcomes for people with movement deficits. Research is required on wearable and smartphone sensor acquisition and location, sensor fusion to generate usable outcomes, integrating outcomes with multimedia for augmented reality presentation, and determining effective real-time methods for integrating quantitative outcomes into clinical practice. This research will build on our recent smartphone application research to augment clinical tests, provided augmented reality analysis of body position, and improved activity classification from smartphone sensors and video. While prototype applications have been developed, pilot tests are needed to verify application performance in practice. Next-phase research will improve smartphone-based mobility activity classification for people with pathological gait (amputee, stroke, etc.), enhance video tools for real-time/augmented reality analysis of human movement, integrate new smartphone sensors into classification algorithms, and provide immediate smartphone-based analysis of human movement that can be used at the point of patient contact.