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海马优化-深度神经网络:基于手势识别的药物坚持监测系统

Palanisamy Amirthalingam1, Yasser Alatawi1, Narmatha Chellamani2

  • 1Department of Pharmacy Practice, Faculty of Pharmacy, University of Tabuk, Tabuk 71491, Saudi Arabia.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
概括

这项研究引入了一种新的基于传感器的系统,使用手势和机器学习来准确预测药物摄入量. 创新的海马优化-深度神经网络 (SHO-DNN) 模型实现了超过98%的药物坚持监测准确度.

关键词:
手的手势手势手势机器学习是机器学习.药物治疗的坚持 药物治疗的坚持传感器 传感器 传感器智能可穿戴设备是一种智能可穿戴设备.

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科学领域:

  • 生物医学工程 生物医学工程
  • 医疗信息学 医疗信息学
  • 机器学习应用 机器学习应用

背景情况:

  • 药物坚持对于治疗成功至关重要,但准确的监测仍然是一个全球性的挑战.
  • 传感器技术和机器学习 (ML) 为持续的患者坚持观察提供了有希望的解决方案.
  • 现有的方法缺乏标准化的技术,阻碍了有效的患者治疗方案管理.

研究的目的:

  • 开发基于传感器的手势识别模型,用于预测药物活动.
  • 创建一个与ML集成的智能传感器设备,用于准确检测药物摄入量.
  • 通过创新的可穿戴技术,加强患者监测.

主要方法:

  • 使用带有三轴陀螺仪,几何和加速度计传感器的智能传感器设备收集手势数据.
  • 一个智能手机应用程序将传感器数据传输到云数据库 (.csv格式).
  • 一个新的海马优化深度神经网络 (SHO-DNN) 模型分类了手势来识别药物摄入.

主要成果:

  • 该SHO-DNN模型实现了高性能指标:98.59%的精度,97.82%的灵敏度,98.69%的精度,98.48%的F1得分.
  • 与现有的可用模型相比,拟议的模型表现出优越的性能.
  • 海马优化技术有效调整了深度神经网络参数,以改善分类.

结论:

  • 开发的基于传感器的手势识别模型是监测药物坚持的高效工具.
  • 这种创新方法提供了一种可靠和准确的方法来跟踪患者的药物使用行为.
  • 这些发现支持这项技术在推进远程医疗保健应用方面的潜力.