Timeseries partitioning is an essential step in most machine-learning driven, sensor-based IoT applications. This paper introduces a sample-efficient, robust, time-series segmentation model and algorithm. We show that by learning a representation specifically with the segmentation objective based on maximum mean discrepancy (MMD), our algorithm can robustly detect time-series events across different applications. Our loss function allows us to infer whether consecutive sequences of samples are drawn from the same distribution (null hypothesis) and determines the change-point between pairs that reject the null hypothesis (i.e., come from different distributions). We demonstrate its applicability in a real-world IoT deployment for ambient-sensing based activity recognition. Moreover, while many works on change-point detection exist in the literature, our model is significantly simpler and can be fully trained in 9-93 seconds on average with little variation in hyperparameters for data across different applications. We empirically evaluate Cadence on four popular change point detection (CPD) datasets where Cadence matches or outperforms existing CPD techniques.

The number of Internet-of-Things (IoT) and edge devices has exploded in the last decade (IoT000; IoT00; AGG04), providing new opportunities to transform everyday people's lives. Coupled with advances in learning technologies (ML00; ML01), these can transform how people interact with their environment. A typical machine learning workflow in sensor-based applications starts with unlabeled data. That data is visualized, featurized, and clustered in search of patterns. Typically, labels are obtained, and subsequent sample-label pairs are used to train a classifier.