一种基于人工智能的疼痛监测系统的开发，该系统使用袜子中的皮肤电导传感器。

IF 6.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Neural Systems Pub Date : 2022-10-01 Epub Date: 2022-09-09 DOI:10.1142/S0129065722500472

Helen Korving, Di Zhou, Huan Xiang, Paula Sterkenburg, Panos Markopoulos, Emilia Barakova

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引用次数: 3

摘要

背景:在自我报告不可行或观察困难的情况下，需要对疼痛进行生理估计。方法:收集30名健康成人的疼痛数据，建立生理性疼痛反应数据库。然后开发了一个模型来分析疼痛数据，并在移动应用程序上可视化人工智能估计的疼痛水平。结果:通过插值一定百分比的相似数据，解决了疼痛分类算法最初的低精度和f1评分问题。讨论:该系统提出了一种使用传感器袜子、人工智能预测器和移动应用程序来评估非交流人群疼痛的新方法。讨论了性能分析和人工智能算法的局限性。

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Development of an AI-Enabled System for Pain Monitoring Using Skin Conductance Sensoring in Socks.

Background: Where self-report is unfeasible or observations are difficult, physiological estimates of pain are needed. Methods: Pain-data from 30 healthy adults were gathered to create a database of physiological pain responses. A model was then developed, to analyze pain-data and visualize the AI-estimated level of pain on a mobile app. Results: The initial low precision and F1-score of the pain classification algorithm were resolved by interpolating a percentage of similar data. Discussion: This system presents a novel approach to assess pain in noncommunicative people with the use of a sensor sock, AI predictor and mobile app. Performance analysis and the limitations of the AI algorithm are discussed.

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来源期刊

International Journal of Neural Systems 工程技术-计算机：人工智能

CiteScore

11.30

自引率

28.80%

发文量

116

审稿时长

24 months

期刊介绍： The International Journal of Neural Systems is a monthly, rigorously peer-reviewed transdisciplinary journal focusing on information processing in both natural and artificial neural systems. Special interests include machine learning, computational neuroscience and neurology. The journal prioritizes innovative, high-impact articles spanning multiple fields, including neurosciences and computer science and engineering. It adopts an open-minded approach to this multidisciplinary field, serving as a platform for novel ideas and enhanced understanding of collective and cooperative phenomena in computationally capable systems.