# Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes

arXiv: https://arxiv.org/abs/2111.12158 · HTML: https://mlsanity.com/paper/2111.12158/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 23 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Evaluating Word Embedding Models: Methods and Experimental Results](https://mlsanity.com/paper/1901.09785/) (2019)
- [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)
- [Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes](https://mlsanity.com/paper/2012.02300/) (2020)
- [Human Activity Recognition using Recurrent Neural Networks](https://mlsanity.com/paper/1804.07144/) (2018)
- [Different Approaches for Human Activity Recognition: A Survey](https://mlsanity.com/paper/1906.05074/) (2019)
- [IoT2Vec: Identification of Similar IoT Devices via Activity Footprints](https://mlsanity.com/paper/1805.07907/) (2018)

## Built on by (most cited first, 12 of 12)

- [Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)](https://mlsanity.com/paper/2405.12368/) (2024)
- [Towards LLM-Powered Ambient Sensor Based Multi-Person Human Activity Recognition](https://mlsanity.com/paper/2407.09529/) (2024)
- [Leveraging Large Language Models for Explainable Activity Recognition in Smart Homes: A Critical Evaluation](https://mlsanity.com/paper/2503.16622/) (2025)
- [AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments](https://mlsanity.com/paper/2506.11773/) (2025)
- [Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes](https://mlsanity.com/paper/2311.09514/) (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)
- [SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning](https://mlsanity.com/paper/2304.09530/) (2023)
- [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)
- [DomusFM: A Foundation Model for Event-Based Behavioral Monitoring in Smart-Homes](https://mlsanity.com/paper/2602.01910/) (2026)
- [LastAct: Trajectory-Guided Latest-Activity Localization for Real-Time Smart-Home Activity Recognition](https://mlsanity.com/paper/2606.00260/) (2026)

## Abstract (arXiv)

Long Short Term Memory LSTM-based structures have demonstrated their efficiency for daily living recognition activities in smart homes by capturing the order of sensor activations and their temporal dependencies. Nevertheless, they still fail in dealing with the semantics and the context of the sensors. More than isolated id and their ordered activation values, sensors also carry meaning. Indeed, their nature and type of activation can translate various activities. Their logs are correlated with each other, creating a global context. We propose to use and compare two Natural Language Processing embedding methods to enhance LSTM-based structures in activity-sequences classification tasks: Word2Vec, a static semantic embedding, and ELMo, a contextualized embedding. Results, on real smart homes datasets, indicate that this approach provides useful information, such as a sensor organization map, and makes less confusion between daily activity classes. It helps to better perform on datasets with competing activities of other residents or pets. Our tests show also that the embeddings can be pretrained on different datasets than the target one, enabling transfer learning. We thus demonstrate that taking into account the context of the sensors and their semantics increases the classification performances and enables transfer learning.