# Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes

arXiv: https://arxiv.org/abs/2012.02300 · HTML: https://mlsanity.com/paper/2012.02300/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 14 of 14)

- [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)
- [Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes](https://mlsanity.com/paper/2111.12158/) (2021)
- [Towards LLM-Powered Ambient Sensor Based Multi-Person Human Activity Recognition](https://mlsanity.com/paper/2407.09529/) (2024)
- [AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments](https://mlsanity.com/paper/2506.11773/) (2025)
- [Too Good To Be True: performance overestimation in (re)current practices for Human Activity Recognition](https://mlsanity.com/paper/2310.11950/) (2023)
- [Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes](https://mlsanity.com/paper/2311.09514/) (2023)
- [Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition](https://mlsanity.com/paper/2404.07245/) (2024)
- [CARE: Contrastive Alignment for ADL Recognition from Event-Triggered Sensor Streams](https://mlsanity.com/paper/2510.16988/) (2025)
- [Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homes](https://mlsanity.com/paper/2406.14446/) (2024)
- [Meta-Decomposition: Dynamic Segmentation Approach Selection in IoT-based Activity Recognition](https://mlsanity.com/paper/2404.11742/) (2024)
- [Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition](https://mlsanity.com/paper/2412.19732/) (2024)
- [DISCOVER: Identifying Patterns of Daily Living in Human Activities from Smart Home Data](https://mlsanity.com/paper/2503.01733/) (2025)
- [LastAct: Trajectory-Guided Latest-Activity Localization for Real-Time Smart-Home Activity Recognition](https://mlsanity.com/paper/2606.00260/) (2026)

## Abstract (arXiv)

Activity recognition in smart homes is essential when we wish to propose automatic services for the inhabitants. However, it poses challenges in terms of variability of the environment, sensorimotor system, but also user habits. Therefore, endto-end systems fail at automatically extracting key features, without extensive pre-processing. We propose to tackle feature extraction for activity recognition in smart homes by merging methods from the Natural Language Processing (NLP) and the Time Series Classification (TSC) domains. We evaluate the performance of our method on two datasets issued from the Center for Advanced Studies in Adaptive Systems (CASAS). Moreover, we analyze the contributions of the use of NLP encoding Bag-Of-Word with Embedding as well as the ability of the FCN algorithm to automatically extract features and classify. The method we propose shows good performance in offline activity classification. Our analysis also shows that FCN is a suitable algorithm for smart home activity recognition and hightlights the advantages of automatic feature extraction.