Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm-start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically relevant behavior anomalies from over 2M sensor readings collected in five smart homes, reflecting 26 health events. Results indicate that indirectly supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.

中文翻译：

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从智能家居数据间接监督临床有意义的健康事件异常检测


异常检测技术可以提取有关异常事件的大量信息。不幸的是，这些方法产生了大量不感兴趣的发现，掩盖了相关的异常现象。在这项工作中，我们通过引入 Isudra（一种来自时间序列数据的相关异常的间接监督检测器）来改进传统的异常检测方法。 Isudra 采用贝叶斯优化来选择时间尺度、特征、基本检测器算法和算法超参数，以增加真阳性检测并减少误报检测。这种优化是由少量示例异常驱动的，从而驱动了间接监督的异常检测方法。此外，我们通过引入热启动方法来增强该方法，该方法可以减少类似问题之间的优化时间。我们验证了 Isudra 从五个智能家居收集的超过 200 万个传感器读数（反映 26 个健康事件）中检测临床相关行为异常的可行性。结果表明，间接监督异常检测在检测跌倒、夜尿、抑郁和虚弱等健康相关异常实例方面优于监督和非监督算法。