ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data

Abstract

Extracting informative and meaningful temporal segments from high-dimensional wearable sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as Human Activity Recognition (HAR), trajectory prediction, gesture recognition, and lifelogging. In this paper, we propose ESPRESSO (Entropy and ShaPe awaRe timE-Series SegmentatiOn), a hybrid segmentation model for multi-dimensional time-series that is formulated to exploit the entropy and temporal shape properties of time-series. ESPRESSO differs from existing methods that focus upon particular statistical or temporal properties of time-series exclusively. As part of model development, a novel temporal representation of time-series $WCAC$ was introduced along with a greedy search approach that estimate segments based upon the entropy metric. ESPRESSO was shown to offer superior performance to four state-of-the-art methods across seven public datasets of wearable and wear-free sensing. In addition, we undertake a deeper investigation of these datasets to understand how ESPRESSO and its constituent methods perform with respect to different dataset characteristics. Finally, we provide two interesting case-studies to show how applying ESPRESSO can assist in inferring daily activity routines and the emotional state of humans.

23 pages, 11 figures, accepted at IMWUT Volume(4) issue(3)

References in corpus (4)

• Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation
• Multi-task Self-Supervised Learning for Human Activity Detection
• Joint segmentation of multivariate time series with hidden process regression for human activity recognition
• Time Series Segmentation through Automatic Feature Learning

Cited by in corpus (5)

• Generative Adversarial Networks for Spatio-temporal Data: A Survey
• Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding
• COCOA: Cross Modality Contrastive Learning for Sensor Data
• ClaSP -- Parameter-free Time Series Segmentation
• PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation