Bhakta Krishan, Camargo Jonathan, Kunapuli Pratik, Childers Lee, Young Aaron
Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA USA.
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA USA.
Mil Med. 2020 Jan 7;185(Suppl 1):490-499. doi: 10.1093/milmed/usz229.
Powered prostheses are a promising new technology that may help people with lower-limb loss improve their ability to perform locomotion tasks. Developing active prostheses requires robust design methodologies and intelligent controllers to appropriately provide assistance to the user for varied tasks in different environments. The purpose of this study was to validate an impedance control strategy for a powered knee and ankle prosthesis using an embedded sensor suite of encoders and a six-axis load cell that would aid an individual in performing common locomotion tasks, such as level walking and ascending/descending slopes.
Three amputees walked on a treadmill and four amputees walked on a ramp circuit to test whether a dual powered knee and ankle prosthesis could generate appropriate device joint kinematics across users.
Investigators found that tuning 2-3 subject-specific parameters per ambulation mode was necessary to render individualized assistance. Furthermore, the kinematic profiles demonstrate invariance to walking speeds ranging from 0.63 to 1.07 m/s and incline/decline angles ranging from 7.8° to 14°.
This work presents a strategy that requires minimal tuning for a powered knee & ankle prosthesis that scales across a nominal range of both walking speeds and ramp slopes.
动力假肢是一项很有前景的新技术,可能有助于下肢缺失者提高其执行移动任务的能力。开发主动式假肢需要强大的设计方法和智能控制器,以便在不同环境中为用户执行各种任务时提供适当的辅助。本研究的目的是使用编码器和六轴测力传感器组成的嵌入式传感器套件,验证一种用于动力膝关节和踝关节假肢的阻抗控制策略,该策略将帮助个体执行常见的移动任务,如平地行走和上下斜坡。
三名截肢者在跑步机上行走,四名截肢者在斜坡电路上行走,以测试双动力膝关节和踝关节假肢是否能在不同用户间产生合适的装置关节运动学。
研究人员发现,每种行走模式调整2至3个特定于个体的参数对于提供个性化辅助是必要的。此外,运动学曲线表明,在0.63至1.07米/秒的行走速度范围以及7.8°至14°的倾斜/下降角度范围内具有不变性。
这项工作提出了一种策略,对于动力膝关节和踝关节假肢,在行走速度和斜坡坡度的标称范围内,该策略所需的调整最少。