从装在鞋子上的加速度计传感器中检测帕金森病,使用通过修改后的元启证优化的卷积神经网络来检测帕金森病
Luka Jovanovic1, Robertas Damaševičius2, Rade Matic3
1Faculty of Technical Sciences, Singidunum University, Belgrade, Serbia.
PeerJ. Computer science
|June 10, 2024
概括
卷积神经网络 (CNN) 显示出使用鞋子传感器行走数据的早期帕金森病诊断的前景. 优化的CNN方法显著提高了检测准确度,提高了患者的生活质量.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 神经退行性疾病显著影响生活质量.
- 早期诊断对于管理疾病进展至关重要,但往往会延迟.
- 为了及时干预,需要使用非侵入性诊断方法.
研究的目的:
- 探索卷积神经网络 (CNN) 以早期检测帕金森病 (PD) 相关的步态结.
- 为了评估CNN的有效性,使用可穿戴陀螺仪数据来分析行走模式.
- 引入和评估一个优化的算法,以提高诊断准确度.
主要方法:
- 利用鞋底中的可穿戴陀螺仪传感器数据来捕捉行走模式.
- 应用卷积神经网络 (CNN) 用于分析和检测异常步态.
- 引入了修改后的鱼优化算法 (MSCHO),以提高分类准确度.
主要成果:
- 与其他优化方法相比,MSCHO算法表现出卓越的性能.
- 在多个数据集中实现了高准确度,精度,灵敏度和F1测量.
- 低误差率证实了拟议的CNN方法对PD检测的有效性.
结论:
- 与先进的优化技术相结合的CNN为早期,非侵入性帕金森病诊断提供了可行的途径.
- 这种方法有可能通过及时治疗显著改善患者的生活质量.
- 可穿戴传感器数据和人工智能为神经退行性疾病诊断提供了一个有希望的未来.
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