Department of Child and Family Studies, Vrije Universiteit Amsterdam, Van der Boechorststraat, 7, Amsterdam, 1081 BT, The Netherlands.
Department of Industrial Design, Eindhoven University of Technology, De Goene Loper 3, Eindhoven, 5612 AE, The Netherlands.
Int J Neural Syst. 2022 Oct;32(10):2250047. doi: 10.1142/S0129065722500472. Epub 2022 Sep 9.
: Where self-report is unfeasible or observations are difficult, physiological estimates of pain are needed. : 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. : The initial low precision and F1-score of the pain classification algorithm were resolved by interpolating a percentage of similar data. : 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.
当自我报告不可行或观察困难时，就需要生理估计疼痛。收集了 30 名健康成年人的疼痛数据，以创建一个生理疼痛反应数据库。然后开发了一个模型，用于分析疼痛数据，并在移动应用程序上可视化人工智能估计的疼痛水平。通过插值类似数据的百分比，解决了疼痛分类算法最初的低精度和 F1 分数问题。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。
