# Action2Activity: Recognizing Complex Activities from Sensor Data

arXiv: https://arxiv.org/abs/1611.01872 · HTML: https://mlsanity.com/paper/1611.01872/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2016.

## Built on by (most cited first, 5 of 5)

- [CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensors](https://mlsanity.com/paper/2202.03283/) (2022)
- [CHARM: A Hierarchical Deep Learning Model for Classification of Complex Human Activities Using Motion Sensors](https://mlsanity.com/paper/2207.07806/) (2022)
- [A Causality-Aware Pattern Mining Scheme for Group Activity Recognition in a Pervasive Sensor Space](https://mlsanity.com/paper/2312.00404/) (2023)
- [Resource-Limited Automated Ki67 Index Estimation in Breast Cancer](https://mlsanity.com/paper/2401.00014/) (2023)
- [Learning Alternative Ways of Performing a Task](https://mlsanity.com/paper/2404.02579/) (2024)

## Abstract (arXiv)

As compared to simple actions, activities are much more complex, but semantically consistent with a human's real life. Techniques for action recognition from sensor generated data are mature. However, there has been relatively little work on bridging the gap between actions and activities. To this end, this paper presents a novel approach for complex activity recognition comprising of two components. The first component is temporal pattern mining, which provides a mid-level feature representation for activities, encodes temporal relatedness among actions, and captures the intrinsic properties of activities. The second component is adaptive Multi-Task Learning, which captures relatedness among activities and selects discriminant features. Extensive experiments on a real-world dataset demonstrate the effectiveness of our work.