在以过程为导向的行为干预中预测目标实现,使用数据驱动的系统识别方法
Sarasij Banerjee1, Rachael T Kha1, Daniel E Rivera1
1Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85287 USA.
概括
个性化行为干预可以通过系统识别来优化. 这项研究使用离散同时扰乱随机近似 (DSPSA) 来建模健康行为变化中的个体差异,改进干预设计.
科学领域:
- 行为科学是一种行为科学.
- 系统工程是系统工程.
- 控制理论 控制理论 控制理论
背景情况:
- 针对健康的行为干预可以是复杂的动态系统.
- 了解个人的动机是优化干预的关键.
- 系统识别提供了模拟这些复杂行为的方法.
研究的目的:
- 为了高效地估计个性化,健康行为的动态模型.
- 开发控制系统,以优化行为干预.
- 将系统识别应用于"简单步行"研究,以增加身体活动.
主要方法:
- 使用离散同时扰动随机近似 (DSPSA) 进行建模.
- 使用自动回归与异源输入 (ARX) 模型进行估计.
- DSPSA为复杂数据集的特征和模型顺序选择提供了便利.
主要成果:
- DSPSA有效估计了个性化的动态行为模型.
- 这项研究确定了激励因素的显著个体变异性.
- DSPSA有效地处理了计算密集型的计算.
结论:
- 个性化动态模型对于有效的行为干预至关重要.
- DSPSA是分析复杂行为数据的强大工具.
- 优化干预需要理解和适应个体差异.
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