# COMPASS: Unsupervised and Online Clustering of Complex Human Activities from Smartphone Sensors

arXiv: https://arxiv.org/abs/2306.15437 · HTML: https://mlsanity.com/paper/2306.15437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 45 references that are in the map.
- First posted on arXiv 27 Jun 2023.

## Builds on (most cited first, 2 of 2)

- [Recognizing Detailed Human Context In-the-Wild from Smartphones and Smartwatches](https://mlsanity.com/paper/1609.06354/) (2016)
- [Edge Computing For Smart Health: Context-aware Approaches, Opportunities, and Challenges](https://mlsanity.com/paper/2004.07311/) (2020)

## Built on by (most cited first, 1 of 1)

- [Chorus: Harmonizing Context and Sensing Signals for Data-Free Model Customization in IoT](https://mlsanity.com/paper/2512.15206/) (2025)

## Abstract (arXiv)

Modern mobile devices are able to provide context-aware and personalized services to the users, by leveraging on their sensing capabilities to infer the activity and situation in which a person is currently involved. Current solutions for context-recognition rely on annotated data and experts’ knowledge to predict the user context. In addition, their prediction ability is strongly limited to the set of situations considered during the model training or definition. However, in a mobile environment, the user context continuously evolves, and it cannot be merely restricted to a set of predefined classes. To overcome these limitations, we propose COMPASS, a novel unsupervised and online clustering algorithm aimed at identifying the user context in mobile environments based on the stream of high-dimensional data generated by smartphone sensors. COMPASScan distinguish an arbitrary number of user’s contexts from the sensors’ data, without defining a priori the collection of expected situations. This key feature makes it a general-purpose solution to provide context-aware features to mobile devices, supporting a broad set of applications. Experimental results on 18 synthetic and 2 real-world datasets show that COMPASS correctly identifies the user context from the sensors’ data stream, and outperforms the state-of-the-art solutions in terms of both clusters configuration and purity. Eventually, we evaluate its performances in terms of execution time and the results show that COMPASS can process 1000 high-dimensional samples in less than 20 seconds, while the reference solutions require about 60 minutes to evaluate the entire dataset. Keywords: Context-awareness, Unsupervised Machine Learning, Online Clustering, Mobile Computing