数据驱动的移动健康:系统识别和混合模型预测控制,提供个性化的体力活动干预措施
Mohamed El Mistiri1, Owais Khan1, César A Martin2
1Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85287 USA.
IEEE open journal of control systems
|July 8, 2025
概括
控制系统原则个性化体育活动干预措施. 控制优化试验 (COT) 框架使用系统识别和混合模型预测控制来创建个性化的行为目标,证明在促进行走行为的有效性.
科学领域:
- 行为医学是一种行为医学.
- 控制系统工程 控制系统工程
- 计算建模计算建模
背景情况:
- 行为医学寻求针对身体活动等健康习惯的个性化干预措施.
- 控制系统为设计适应性和个性化干预提供了一个框架.
研究的目的:
- 在控制优化试验 (COT) 框架内应用系统识别和混合模型预测控制.
- 设计个性化的行为干预措施,以促进身体活动.
- 使用参与者数据证明COT方法的有效性.
主要方法:
- 系统识别以从参与者数据中估计ARX模型.
- 开发基于卡尔曼波器的三度自由度混合模型预测控制器 (3DoF-KF HMPC).
- 闭环模拟 (名义和蒙特卡洛) 来评估控制器的性能和稳定性.
主要成果:
- 选择的ARX模型平衡了预测能力和适合个人参与者的数据.
- 3DoF-KF HMPC为体育活动提供了"雄心勃勃但可行的"目标.
- 模拟证实了控制器在模型不匹配的情况下的性能和稳定性.
结论:
- 控制优化试验 (COT) 方法为个性化行为干预提供了概念证明.
- 这种方法表明,它有望促进对体力活动的持续参与.
- 该方法目前正在YourMove.临床试验中进行评估.
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