Title: Embedded elbow vein blood collection robot system based on artificial intelligence technology

Authors: Huanwen Wang; Xiaoya Zhang; Hao Chen

Addresses: College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China ' College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China ' College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China

Abstract: With the development and advancement of healthcare, venous blood collection is becoming an essential tool for the initial screening of patients for disease. In current clinical practice, it is mainly done manually by experienced physicians or nurses. To effectively reduce the repetitive work of healthcare workers, we designed an embedded elbow vein recognition system artificial intelligence technology (AI)-based. Firstly, a prototype of vein vascular identification based on near infrared (NIR) spectroscopy was completed by combining the analysis of vein vascular acquisition data with NIR spectroscopy. Secondly, a deep learning-based image classification algorithm is implemented to classify elbow cover images. Furthermore, we used a CNN-based semantic segmentation algorithm to intelligently extract the region of interest for the veins in the elbow region. Finally, we validated the system in a real environment and achieved high recognition accuracy for elbow veins through effective training and testing strategies.

Keywords: convolutional neural network; elbow vein vessel identification; medical robot; intelligent embedded systems; venipuncture.

DOI: 10.1504/IJES.2023.134126

International Journal of Embedded Systems, 2023 Vol.16 No.1, pp.36 - 46

Received: 08 Sep 2022
Received in revised form: 13 Mar 2023
Accepted: 29 Mar 2023
Published online: 11 Oct 2023 *

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