Switching-State Dynamical Modeling of Daily Behavioral Data
Switching-State Dynamical Modeling of Daily Behavioral Data
复制标题
日常行为数据的切换状态动态建模
DOI：
10.1007/s41666-018-0017-x
复制
发表时间：
2018
影响因子：
5.9
通讯作者：
Qian, Xiaoning
中科院分区：
文献类型：
--
作者：
Ardywibowo, Randy;Huang, Shuai;Gui, Shupeng;Xiao, Cao;Cheng, Yu;Liu, Ji;Qian, Xiaoning
新兴的可穿戴和环境传感器技术为健康专业人员提供了前所未有的能力，可以持续收集人类行为数据，用于健康监测和管理。这使得新的解决方案能够缓解肥胖等全球新出现的健康问题。对于这种突发的动态传感器数据，关键是开发适当的数学模型和计算方法，将收集的数据转换为对潜在健康动态的准确表征，从而实现对健康状态变化的更可靠的个性化监控、预测和干预。除了解决传感器行为数据分析中常见的分析挑战，如缺失值和异常值，我们还专注于对异类动力学进行建模，以更好地捕获不同条件下的健康状态变化，这可能会导致更有效的状态依赖干预策略。我们在真实世界的日常行为数据上实现了不同复杂程度的切换状态动态系统模型。通过对这些模型的评估实验，验证了动态异质性建模的重要性，同时进行缺失值补充和离群点检测，以获得更好地预测健康状态变化的可解释的健康动态模型。
Emerging wearable and environmental sensor technologies provide health professionals with unprecedented capacity to continuously collect human behavioral data for health monitoring and management. This enables new solutions to mitigate globally emerging health problems such as obesity. With such outburst of dynamic sensor data, it is critical that appropriate mathematical models and computational methods are developed to translate the collected data into accurate characterization of the underlying health dynamics, enabling more reliable personalized monitoring, prediction, and intervention of health status changes. In addition to addressing common analytic challenges in analyzing sensor behavioral data, such as missing values and outliers, we focus on modeling heterogeneous dynamics to better capture health status changes under different conditions, which may lead to more effective state-dependent intervention strategies. We implement switching-state dynamic system models with different complexity levels on real-world daily behavioral data. Evaluation experiments of these models are conducted to demonstrate the importance of modeling the dynamic heterogeneity, as well as simultaneously conducting missing value imputation and outlier detection in achieving interpretable health dynamic models with better prediction of health status changes.
DOI：
10.1001/jama.2014.732
发表时间：
2014-02-26
期刊：
JAMA
影响因子：
--
作者：
Ogden CL;Carroll MD;Kit BK;Flegal KM
通讯作者：
Flegal KM
DOI：
10.5120/2619-3544
发表时间：
2011-05
期刊：
International Journal of Computer Applications
影响因子：
--
作者：
R. Somasundaram;R. Nedunchezhian
通讯作者：
R. Somasundaram;R. Nedunchezhian
DOI：
10.1136/ebmh.11.4.102
发表时间：
2008-10
期刊：
Evidence Based Mental Health
影响因子：
--
作者：
P. Cochat;L. Vaucoret;J. Sarles
通讯作者：
P. Cochat;L. Vaucoret;J. Sarles