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基于任务的外骨骼与按需辅助算法的集成，用于以患者为中心的肘部康复。

Integration of Task-Based Exoskeleton with an Assist-as-Needed Algorithm for Patient-Centered Elbow Rehabilitation.

机构信息

Department of Mechanical Engineering, Wichita State University, Wichita, KS 67260, USA.

出版信息

Sensors (Basel). 2023 Feb 23;23(5):2460. doi: 10.3390/s23052460.

DOI:10.3390/s23052460
PMID:36904662
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10006945/
Abstract

This research presents an Assist-as-Needed (AAN) Algorithm for controlling a bio-inspired exoskeleton, specifically designed to aid in elbow-rehabilitation exercises. The algorithm is based on a Force Sensitive Resistor (FSR) Sensor and utilizes machine-learning algorithms that are personalized to each patient, allowing them to complete the exercise by themselves whenever possible. The system was tested on five participants, including four with Spinal Cord Injury and one with Duchenne Muscular Dystrophy, with an accuracy of 91.22%. In addition to monitoring the elbow range of motion, the system uses Electromyography signals from the biceps to provide patients with real-time feedback on their progress, which can serve as a motivator to complete the therapy sessions. The study has two main contributions: (1) providing patients with real-time, visual feedback on their progress by combining range of motion and FSR data to quantify disability levels, and (2) developing an assist-as-needed algorithm for rehabilitative support of robotic/exoskeleton devices.

摘要

本研究提出了一种按需辅助（AAN）算法，用于控制仿生外骨骼，专门设计用于辅助肘部康复运动。该算法基于力敏电阻（FSR）传感器，并利用针对每个患者进行个性化的机器学习算法，使他们尽可能在自己的情况下完成运动。该系统在五名参与者（包括四名脊髓损伤患者和一名杜氏肌营养不良症患者）身上进行了测试，准确率为 91.22%。除了监测肘部运动范围外，该系统还使用肱二头肌的肌电图信号为患者提供有关进展的实时反馈，这可以作为完成治疗的动力。该研究有两个主要贡献：（1）通过将运动范围和 FSR 数据相结合来量化残疾程度，为患者提供有关进展的实时、可视反馈；（2）为机器人/外骨骼设备的康复支持开发按需辅助算法。

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