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

arXiv: https://arxiv.org/abs/2008.03230 · HTML: https://mlsanity.com/paper/2008.03230/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [ClaSP -- Parameter-free Time Series Segmentation](https://mlsanity.com/paper/2207.13987/) (2022)
- [COCOA: Cross Modality Contrastive Learning for Sensor Data](https://mlsanity.com/paper/2208.00467/) (2022)
- [Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables](https://mlsanity.com/paper/2105.06637/) (2021)
- [Normalizing self-supervised learning for provably reliable Change Point Detection](https://mlsanity.com/paper/2410.13637/) (2024)
- [PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation](https://mlsanity.com/paper/2506.11170/) (2025)

## Abstract (arXiv)

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.