系统识别和混合模型预测控制在个性化mHealth干预体育活动的系统识别和混合模型预测控制
Mohamed El Mistiri1, Owais Khan1, Daniel E Rivera1
1Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85287 USA.
概括
这项研究引入了一项控制优化试验 (COT),以个性化对身体活动 (PA) 的行为干预. COT方法使用系统识别和控制工程来设计有效的健康行为改变策略.
科学领域:
- 行为医学是一种行为医学.
- 控制工程 控制工程 控制工程
- 系统识别系统识别系统
背景情况:
- 行为医学旨在促进身体活动 (PA) 等健康行为.
- 个性化干预对于持续参与PA至关重要.
- 控制系统原则为设计行为干预提供了一个新的框架.
研究的目的:
- 提出一种新的控制优化试验 (COT) 形式主义,用于设计行为干预.
- 为了说明COT阶段,使用来自"Just Walk"干预的数据.
- 展示系统识别和控制工程在行为医学中的应用.
主要方法:
- 使用系统识别来估计个人参与者的ARX模型.
- 根据控制器实现的加权标准选择了表现最好的模型.
- 制定了一个混合型预测控制 (MPC) 控制器,具有3度自由度 (3DoF) 调.
主要成果:
- 通过使用来自"Just Walk"干预的参与者数据来估计和验证ARX模型.
- 选择的模型作为MPC控制器的内部模型.
- 模拟显示了控制器在闭环设置中的性能.
结论:
- 控制优化试验 (COT) 方法为设计个性化行为干预提供了概念证明.
- 这种方法有效地平衡了对身体活动干预措施的要求.
- 目前,COT方法正在"YourMove"临床试验中进行评估.
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