# Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence Model

arXiv: https://arxiv.org/abs/1907.05597 · HTML: https://mlsanity.com/paper/1907.05597/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2019.

## Built on by (most cited first, 8 of 8)

- [A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning](https://mlsanity.com/paper/2111.04418/) (2021)
- [Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)](https://mlsanity.com/paper/2405.12368/) (2024)
- [AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments](https://mlsanity.com/paper/2506.11773/) (2025)
- [ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence](https://mlsanity.com/paper/2301.07051/) (2023)
- [Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homes](https://mlsanity.com/paper/2406.14446/) (2024)
- [MARAuder's Map: Motion-Aware Real-time Activity Recognition with Layout-Based Trajectories](https://mlsanity.com/paper/2511.05773/) (2025)
- [Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition](https://mlsanity.com/paper/2412.19732/) (2024)
- [LastAct: Trajectory-Guided Latest-Activity Localization for Real-Time Smart-Home Activity Recognition](https://mlsanity.com/paper/2606.00260/) (2026)

## Abstract (arXiv)

Recognizing activities of daily living (ADLs) plays an essential role in analyzing human health and behavior. The widespread availability of sensors implanted in homes, smartphones, and smart watches have engendered collection of big datasets that reflect human behavior. To obtain a machine learning model based on these data,researchers have developed multiple feature extraction methods. In this study, we investigate a method for automatically extracting universal and meaningful features that are applicable across similar time series-based learning tasks such as activity recognition and fall detection. We propose creating a sequence-to-sequence (seq2seq) model to perform this feature learning. Beside avoiding feature engineering, the meaningful features learned by the seq2seq model can also be utilized for semi-supervised learning. We evaluate both of these benefits on datasets collected from wearable and ambient sensors.