NRI: Balance Recovery Control for Amputees Using Powered Leg Prostheses

截肢者经常跌倒，代价高昂，并对生活质量产生负面影响。2005年，美国有100万人下肢截肢。这些人中约有10%的人至少经历过一次跌倒，导致严重受伤，据估计，美国医疗保健系统每年的成本约为11亿美元。由于肥胖和糖尿病发病率的上升，到2050年，这一数字预计将翻两番。截肢者意识到他们摔倒的风险增加，导致行动能力和社交活动减少。大约一半的截肢者表示害怕摔倒。同样，许多人将无法在不平坦的地形上行走或没有稳定步态辅助装置列为影响他们生活质量的主要限制。这些事实突出了不平衡所带来的挑战，并表明改善截肢者运动平衡恢复将显著提高下肢截肢者的生活质量，并降低相关的医疗费用。机器人义肢的新兴领域为解决这一问题提供了新的义肢设计和控制策略。该项目试图利用这一机会。它将人类神经肌肉控制系统的计算模型与新型动力膝关节和踝关节假肢的设计和生物力学步态分析相结合，建立了新的动力假肢控制范式，大大提高了截肢者从大干扰中恢复的能力。在商用设备中实施这些控制技术，可以降低跌倒率和对跌倒的恐惧，从而改善数百万人的机动性和生活质量。该项目的其他好处包括对教育的支持。研究生和本科生将接受培训和指导，研究工具和成果将整合到课程作业中，包括本项目开发的用于研究人体运动假肢控制和干扰恢复的软件和硬件工具。此外，该项目通过在线发布和免费提供仿真代码、控制实现代码和硬件设计，支持研究成果的传播。这个项目的总体目标是测试一个假设，即受人类运动控制启发的反射式假肢控制策略，可以大大改善膝盖以上截肢者在行走时的平衡恢复。平衡恢复已经发展成为一个重要的研究领域，因为跌倒损伤是老龄化社会中损伤、残疾和死亡的主要原因之一。下肢截肢者尤其有摔倒的危险，因为目前的假肢在从意外干扰中恢复时只能提供有限的功能。该项目结合了计算神经力学、机器人假肢和生物力学步态分析的方法，以确定假肢控制策略，帮助膝盖以上的截肢者在受到诸如绊倒、滑倒和推挤等大干扰后恢复平衡。现有的人体运动反射控制模型适用于截肢者的步态，包括动力假肢反馈控制算法的理论研究和模拟实验中截肢者恢复行为的预测。开发了动力膝踝假体的原型，包括用于在跑步机上快速人在环控制设计和评估的系留假体模拟器，以及允许在实验室外进行评估的移动假体。在反射控制模型中识别的控制算法被嵌入到这些原型中，并在膝盖以上截肢者的平衡恢复实验中被系统地评估。证实该假设的结果可以为动力假肢建立新的控制范例，并使实际控制器能够改善截肢者步态的平衡恢复。此外，该项目将推进人体平衡恢复的理论模型，以及机器人膝盖-脚踝假肢的控制算法和硬件设计。

Falls among amputees are frequent, costly and negatively impact quality of life. In 2005, there were one million people in the United States with lower limb amputation. About 10 percent of these people experience at least one fall that results in serious injury, resulting in estimated costs to the United States health care system of about $1.1 billion annually. This number is expected to quadruple by the year 2050 due to increasing rates of obesity and diabetes. People with amputation are conscious of their increased fall risk, leading to reduced mobility and social activity. About half of the amputee population reports a fear of falling. Similarly large numbers list as major limitations to their quality of life the inability to walk on uneven terrain or without a stabilizing gait aid. These facts highlight the challenge imposed by imbalance, and suggest that improving balance recovery in amputee locomotion would significantly improve the quality of life of lower-limb amputees as well as reduce related health care costs. The emerging field of robotic prosthetics provides the opportunity to attack this problem with novel prosthesis designs and control strategies. The project seeks to take advantage of this opportunity. It combines computational models of the human neuromuscular control system with novel designs of powered knee-and-ankle prostheses and biomechanical gait analysis to establish new control paradigms for powered prostheses that substantially improve the ability of amputees to recover from large disturbances. Implemented in commercial devices, these control techniques could reduce fall rates and fear of falling, thereby improving the mobility and quality of life for millions of people. Other benefits of the project include the support of education. Graduate and undergraduate students will be trained and mentored, and research tools and outcomes will be integrated into coursework, including the software and hardware tools developed in this project for studying prosthesis control and disturbance recovery in human locomotion. In addition, the project supports the dissemination of research results by publishing and freely providing simulation code, control implementation code, and hardware designs online.The overarching goal of this project is to test the hypothesis that a reflex-like prosthesis control strategy inspired by human motor control substantially improves the balance recovery for above-knee amputees during walking. Balance recovery has evolved into a major research area as fall-connected injuries are one of the main causes of impairment, disability and death in aging societies. Lower limb amputees are especially at risk of falling as current prosthetic limbs provide only limited functionality for recovering from unexpected disturbances. The project combines methods from computational neuromechanics, robotic prosthetics, and biomechanical gait analysis to identify prosthesis control strategies that help above-knee amputees recover balance after large disturbances such as trips, slips and pushes. An existing reflex control model of human locomotion is adapted to amputee gait, involving theoretical research on feedback control algorithms for powered prosthetic limbs and predictions of amputee recovery behavior in simulated experiments. Prototypes of powered knee-ankle prostheses are developed, including a tethered prosthesis emulator for rapid human-in-the-loop control design and evaluation on a treadmill, and a mobile prosthesis allowing evaluation outside the laboratory. Control algorithms identified in the reflex control model are embedded in these prototypes and systematically evaluated in balance recovery experiments with above-knee amputees. An outcome confirming the hypothesis could establish new control paradigms for powered prostheses and enable practical controllers for improved balance recovery in amputee gait. In addition, the project will advance theoretical models of human balance recovery as well as control algorithms and hardware designs for robotic knee-ankle prostheses.