论文标题
使用物联网和机器学习的个性化情感检测
Personalized Emotion Detection using IoT and Machine Learning
论文作者
论文摘要
医学互联网是最近在医学方面的技术进步，对提供对健康指标的实时监控非常有帮助。本文提出了一种无创的物联网系统，该系统跟踪患者的情绪，尤其是患有自闭症谱系障碍的情绪。通过一些负担得起的传感器和云计算服务，对个人的心率进行监测和分析，以研究每分钟汗水和心跳变化的影响，以造成不同的情绪。在个人的正常休息条件下，提议的系统可以使用机器学习算法检测正确的情绪，其精度高达92％。拟议方法的结果与医学物联网中最新的解决方案相媲美。
The Medical Internet of Things, a recent technological advancement in medicine, is incredibly helpful in providing real-time monitoring of health metrics. This paper presents a non-invasive IoT system that tracks patients' emotions, especially those with autism spectrum disorder. With a few affordable sensors and cloud computing services, the individual's heart rates are monitored and analyzed to study the effects of changes in sweat and heartbeats per minute for different emotions. Under normal resting conditions of the individual, the proposed system could detect the right emotion using machine learning algorithms with a performance of up to 92% accuracy. The result of the proposed approach is comparable with the state-of-the-art solutions in medical IoT.
