Practical Design of an Upper Prosthetic Limb Using Three Dimensional Printer with an Artificial Intelligence Based Controller

A. Elbreki, Safa Ramdan, Faisal Mohamed, Khadija Alshari, Zakariya Rajab, B. Elhub
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引用次数: 1

Abstract

A noticeable rate of people suffer from a disability due to war and other reasons. One group of disabled people is those who have lost their arms, which makes it challenging to perform basic tasks. This paper proposes a solution that will make life easier for amputees. The study is based on the EMG (Electromyography) signal acquired from muscles and the movement detected using HMI (Human Machine Interfacing). The practical arm is designed with a computer-aided design using SolidWorks software. A 3D printer FDM (Fused Deposition Modeling) with Polylactic Acid “PLA” was used to fabricate the Prosthetic Limb using an open-source prototype named Hackberry hand from Exiii Company. An actual circuit design is arranged using simple tools that cost more minor and consists of simple sensors, motors, and Arduino Nano. The findings show that the database extracted 416$\times$ 4 is presproced classified and tested using KNN (K nearest neighbor) with 94% rate, and obtained 95% with SVM method to recognize four different motions raising the index finger, holding a card with the thumb and forefinger, fist and spread.
基于人工智能控制器的三维打印机上肢假肢的实用设计
由于战争和其他原因而致残的人占相当大的比例。其中一种残疾人是那些失去了手臂的人,这使得他们很难完成基本的任务。这篇文章提出了一个解决方案,将使截肢者的生活更轻松。这项研究是基于从肌肉获得的肌电图信号和使用人机界面检测到的运动。采用SolidWorks软件进行了实用机械臂的计算机辅助设计。使用Exiii公司的开源原型“Hackberry hand”,使用带有聚乳酸“PLA”的3D打印机FDM(熔融沉积建模)来制造假肢。实际的电路设计使用简单的工具,成本更小,由简单的传感器,电机和Arduino Nano组成。结果表明,对提取的416$\times$ 4的数据库进行KNN (K最近邻)预处理分类和测试,识别率为94%,支持向量机方法对举起食指、拇指和食指拿牌、拳头和摊开四种不同动作的识别率为95%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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