# Context-driven Active and Incremental Activity Recognition

arXiv: https://arxiv.org/abs/1906.03033 · HTML: https://mlsanity.com/paper/1906.03033/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

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

- [Large Language Models are Zero-Shot Recognizers for Activities of Daily Living](https://mlsanity.com/paper/2407.01238/) (2024)
- [Personalized Semi-Supervised Federated Learning for Human Activity Recognition](https://mlsanity.com/paper/2104.08094/) (2021)
- [Using Large Language Models to Compare Explainable Models for Smart Home Human Activity Recognition](https://mlsanity.com/paper/2408.06352/) (2024)
- [Neuro-Symbolic Approaches for Context-Aware Human Activity Recognition](https://mlsanity.com/paper/2306.05058/) (2023)
- [ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models](https://mlsanity.com/paper/2403.06586/) (2024)
- [Semantically Encoding Activity Labels for Context-Aware Human Activity Recognition](https://mlsanity.com/paper/2504.07916/) (2025)

## Abstract (arXiv)

Human activity recognition based on mobile device sensor data has been an active research area in mobile and pervasive computing for several years. While the majority of the proposed techniques are based on supervised learning, semi-supervised approaches are being considered to significantly reduce the size of the training set required to initialize the recognition model. These approaches usually apply self-training or active learning to incrementally refine the model, but their effectiveness seems to be limited to a restricted set of physical activities. We claim that the context which surrounds the user (e.g., semantic location, proximity to transportation routes, time of the day) combined with common knowledge about the relationship between this context and human activities could be effective in significantly increasing the set of recognized activities including those that are difficult to discriminate only considering inertial sensors, and the ones that are highly context-dependent. In this paper, we propose CAVIAR, a novel hybrid semi-supervised and knowledge-based system for real-time activity recognition. Our method applies semantic reasoning to context data to refine the prediction of a semi-supervised classifier. The context-refined predictions are used as new labeled samples to update the classifier combining self-training and active learning techniques. Results on a real dataset obtained from 26 subjects show the effectiveness of the context-aware approach both on the recognition rates and on the number of queries to the subjects generated by the active learning module. In order to evaluate the impact of context reasoning, we also compare CAVIAR with a purely statistical version, considering features computed on context data as part of the machine learning process.