# Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition

arXiv: https://arxiv.org/abs/1510.02071 · HTML: https://mlsanity.com/paper/1510.02071/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2015.

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

- [Towards Unified Surgical Skill Assessment](https://mlsanity.com/paper/2106.01035/) (2021)
- [Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)](https://mlsanity.com/paper/2405.12368/) (2024)
- [Video-based assessment of intraoperative surgical skill](https://mlsanity.com/paper/2205.06416/) (2022)
- [Bag of States: A Non-sequential Approach to Video-based Engagement Measurement](https://mlsanity.com/paper/2301.06730/) (2023)
- [CPR-Coach: Recognizing Composite Error Actions based on Single-class Training](https://mlsanity.com/paper/2309.11718/) (2023)
- [Game of LLMs: Discovering Structural Constructs in Activities using Large Language Models](https://mlsanity.com/paper/2406.13777/) (2024)

## Abstract (arXiv)

We present data-driven techniques to augment Bag of Words (BoW) models, which allow for more robust modeling and recognition of complex long-term activities, especially when the structure and topology of the activities are not known a priori. Our approach specifically addresses the limitations of standard BoW approaches, which fail to represent the underlying temporal and causal information that is inherent in activity streams. In addition, we also propose the use of randomly sampled regular expressions to discover and encode patterns in activities. We demonstrate the effectiveness of our approach in experimental evaluations where we successfully recognize activities and detect anomalies in four complex datasets.