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基于模糊逻辑的智能仿生踝地形识别研究。

Research on Terrain Identification of the Smart Prosthetic Ankle by Fuzzy Logic.

出版信息

IEEE Trans Neural Syst Rehabil Eng. 2019 Sep;27(9):1801-1809. doi: 10.1109/TNSRE.2019.2933874. Epub 2019 Aug 8.

DOI:10.1109/TNSRE.2019.2933874
PMID:31398124
Abstract

This research suggests a fuzzy-logic based terrain identification method and the smart prosthetic ankle system, which automatically controls its ankle angle, based on the detected terrain environment, to assist comfortable gait performance of transtibial amputee. Suggested terrain identification method uses shank angle from three different stages of the stance phase in gait cycle (foot-flat, heel-strike, and toe-off) as input for the fuzzy-logic calculation, and detects five different terrain environment (flat, up-slope, down-slope, up-stairs, and down-stairs) within a single step of gait. Suggested smart prosthetic ankle system comprises of 1) load-cell to measure GRF (ground reaction force), 2) IMU (inertial measurement unit) sensor to measure shank angle, 3) actuator and four bar-linkage mechanism to control ankle angle accordingly for detected terrain environment, and 4) MCU (microcontroller unit) to carry out calculations and control algorithm for ankle actuation. To verify the accuracy of the terrain identification method of the system, the experiment was conducted, which consisted of four transtibial amputees to walk on five different terrain conditions, and the result has shown 97.5% detection accuracy. Compared to previous studies, our suggested smart prosthetic ankle system, along with its terrain identification algorithm, uses lesser number of sensors and step cycle to accurately detect gait environment, which may lead to providing better gait assistance and practical convenience for transtibial amputees.

摘要

本研究提出了一种基于模糊逻辑的地形识别方法和智能仿生踝关节系统,该系统根据检测到的地形环境自动控制踝关节角度,帮助胫骨截肢者实现舒适的步态。所提出的地形识别方法使用步态周期中三个不同阶段(足放平、脚跟触地和脚趾离地)的小腿角度作为模糊逻辑计算的输入,并在单个步态步幅内检测五种不同的地形环境(平地、上坡、下坡、上楼梯和下楼梯)。所提出的智能仿生踝关节系统包括 1)测力传感器测量地面反作用力(GRF),2)惯性测量单元(IMU)传感器测量小腿角度,3)执行器和四连杆机构根据检测到的地形环境相应地控制踝关节角度,4)微控制器单元(MCU)进行踝关节致动的计算和控制算法。为了验证系统地形识别方法的准确性,进行了实验,实验由四名胫骨截肢者在五种不同的地形条件下进行,结果表明检测准确率为 97.5%。与之前的研究相比,我们提出的智能仿生踝关节系统及其地形识别算法使用更少的传感器和步幅周期来准确检测步态环境,这可能为胫骨截肢者提供更好的步态辅助和实际便利。

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Research on Terrain Identification of the Smart Prosthetic Ankle by Fuzzy Logic.基于模糊逻辑的智能仿生踝地形识别研究。
IEEE Trans Neural Syst Rehabil Eng. 2019 Sep;27(9):1801-1809. doi: 10.1109/TNSRE.2019.2933874. Epub 2019 Aug 8.
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Low limb prostheses and complex human prosthetic interaction: A systematic literature review.下肢假肢与复杂的人体假肢交互作用:一项系统文献综述。
Front Robot AI. 2023 Feb 13;10:1032748. doi: 10.3389/frobt.2023.1032748. eCollection 2023.
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Gait Alteration in Individual with Limb Loss: The Role of Inertial Sensors.
肢体缺失者步态改变:惯性传感器的作用。
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