# A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning

arXiv: https://arxiv.org/abs/2111.04418 · HTML: https://mlsanity.com/paper/2111.04418/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 118 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Playing for Benchmarks](https://mlsanity.com/paper/1709.07322/) (2017)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging](https://mlsanity.com/paper/1910.11162/) (2019)
- [Transfer learning for time series classification](https://mlsanity.com/paper/1811.01533/) (2018)
- [Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games](https://mlsanity.com/paper/2107.05617/) (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)
- [Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence Model](https://mlsanity.com/paper/1907.05597/) (2019)
- [Online Human Activity Recognition Employing Hierarchical Hidden Markov Models](https://mlsanity.com/paper/1903.04820/) (2019)
- [Different Approaches for Human Activity Recognition: A Survey](https://mlsanity.com/paper/1906.05074/) (2019)
- [Concurrent Activity Recognition with Multimodal CNN-LSTM Structure](https://mlsanity.com/paper/1702.01638/) (2017)
- [Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy](https://mlsanity.com/paper/2102.09854/) (2021)

## Built on by (most cited first, 21 of 21)

- [Xiaomi MiMo-VL-Miloco Technical Report](https://mlsanity.com/paper/2512.17436/) (2025)
- [Transfer Learning in Human Activity Recognition: A Survey](https://mlsanity.com/paper/2401.10185/) (2024)
- [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)
- [Hierarchical and Multimodal Data for Daily Activity Understanding](https://mlsanity.com/paper/2504.17696/) (2025)
- [AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments](https://mlsanity.com/paper/2506.11773/) (2025)
- [Creation of AI-driven Smart Spaces for Enhanced Indoor Environments -- A Survey](https://mlsanity.com/paper/2412.14708/) (2024)
- [A Survey on Multi-Resident Activity Recognition in Smart Environments](https://mlsanity.com/paper/2304.12304/) (2023)
- [Game of LLMs: Discovering Structural Constructs in Activities using Large Language Models](https://mlsanity.com/paper/2406.13777/) (2024)
- [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)
- [On the recognition of the game type based on physiological signals and eye tracking](https://mlsanity.com/paper/2310.17383/) (2023)
- [Meta-Decomposition: Dynamic Segmentation Approach Selection in IoT-based Activity Recognition](https://mlsanity.com/paper/2404.11742/) (2024)
- [An Automated Approach to Collecting and Labeling Time Series Data for Event Detection Using Elastic Node Hardware](https://mlsanity.com/paper/2407.11042/) (2024)
- [Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition](https://mlsanity.com/paper/2412.19732/) (2024)
- [GNN-XAR: A Graph Neural Network for Explainable Activity Recognition in Smart Homes](https://mlsanity.com/paper/2502.17999/) (2025)
- [DISCOVER: Identifying Patterns of Daily Living in Human Activities from Smart Home Data](https://mlsanity.com/paper/2503.01733/) (2025)
- [Evaluating BiLSTM and CNN+GRU Approaches for Human Activity Recognition Using WiFi CSI Data](https://mlsanity.com/paper/2506.11165/) (2025)
- [Plural Voices, Single Agent: Towards Inclusive AI in Multi-User Domestic Spaces](https://mlsanity.com/paper/2510.19008/) (2025)
- [Evaluating Few-Shot Temporal Reasoning of LLMs for Human Activity Prediction in Smart Environments](https://mlsanity.com/paper/2602.11176/) (2026)
- [LastAct: Trajectory-Guided Latest-Activity Localization for Real-Time Smart-Home Activity Recognition](https://mlsanity.com/paper/2606.00260/) (2026)

## Abstract (arXiv)

Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. But new challenges are emerging every day. In this paper, we present recent algorithms, works, challenges and taxonomy of the field of human activity recognition in a smart home through ambient sensors. Moreover, since activity recognition in smart homes is a young field, we raise specific problems, missing and needed contributions. But also propose directions, research opportunities and solutions to accelerate advances in this field.