Doubly-online changepoint detection for monitoring health status during sports activities
运动健康状态监测的双重在线变点检测AI 解读 ↓
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运动健康状态监测的双重在线变点检测
- 研究问题
- 智能手表记录连续跑步活动时，如何同时处理活动之间、单次活动内部及不同变量之间的依赖，并实时判断当前活动是否发生分布变化？
- 方法与思路
- 作者结合未知分段数量的变点模型与高斯状态空间模型，使用在线 EM 和序贯蒙特卡洛近似，持续更新当前活动的变点概率。
- 材料陈述的贡献
- 摘要提出双重在线框架：既处理陆续到达的活动序列，也利用活动内实时观测估计当前活动为变点的概率，并显式考虑多来源依赖。
- 阅读关注点
- 核对活动间变点与活动内状态更新的区别、在线 EM 的更新规则，以及变点概率校准、误报率和不同依赖结构的可识别性。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未报告检测准确率或验证结果，分布变化不能直接区分身体不适、停训和设备故障。
原文摘要
We provide an online framework for analyzing data recorded by smart watches during running activities. In particular, we focus on identifying variations in the behavior of one or more measurements caused by changes in physical condition, such as physical discomfort, periods of prolonged de-training, or even the malfunction of measuring devices. Our framework considers data as a sequence of running activities represented by multivariate time series of physical and biometric data. We combine classical changepoint detection models with an unknown number of components with Gaussian state space models to detect distributional changes between a sequence of activities. The model considers multiple sources of dependence due to the sequential nature of subsequent activities, the autocorrelation structure within each activity, and the contemporaneous dependence between different variables. We provide an online Expectation-Maximization (EM) algorithm involving a sequential Monte Carlo (SMC) approximation of changepoint predicted probabilities. As a byproduct of our model assumptions, our proposed approach processes sequences of multivariate time series in a doubly-online framework. While classical changepoint models detect changes between subsequent activities, the state space framework coupled with the online EM algorithm provides the additional benefit of estimating the real-time probability that a current activity is a changepoint.