人类活动识别使用混合型冠状病毒疾病优化算法用于医疗事物互联网
Asmaa M Khalid1, Doaa Sami Khafaga2, Eman Abdullah Aldakheel2
1Information Technology Department, Faculty of Computers & Informatics, Zagazig University, Zagazig 44519, Egypt.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了使用智能手机传感器数据进行人类活动识别 (HAR) 的混合优化算法. 这种新的方法有效地减少了数据的维度,大大提高了HAR的准确性.
科学领域:
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 智能手机在医疗保健中越来越多地用于通过传感器收集数据,支持诸如老年护理和疾病诊断等应用程序.
- 来自智能手机的高维传感器数据为准确的人类活动识别 (HAR) 提出了挑战.
- 并非所有来自智能手机传感器的功能对HAR来说都同样有价值.
研究的目的:
- 为人类活动识别 (HAR) 提出一个高效的特征选择算法.
- 通过识别最相关的传感器数据特征来提高HAR的性能.
- 为应对智能手机来源的医疗保健数据中高维度的挑战.
主要方法:
- 开发了一种混合算法,将冠状病毒疾病优化算法 (COVIDOA) 与模拟化 (SA) 结合起来.
- 混合方法旨在提高特征选择效率,并在优化中克服局部最佳.
- 该算法专注于选择相关的特征子集,以改进 HAR.
主要成果:
- 拟议的算法使用UCI-HAR数据集进行了评估.
- 性能与七个已建立的特征选择算法进行了比较.
- 关键指标包括适应性,标准偏差,准确性,子集大小和处理时间.
结论:
- 开发的混合优化算法显著优于现有的HAR技术.
- 在人类活动识别方面,平均准确率为97.82%.
- 显示了52.7%的显著特征选择减少比率.
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