mlsanityData to 7 Oct 2026

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

arXiv 2111.04418 ↗First posted 18 Oct 2021cs.HC cs.AI cs.LGcited by 21

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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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 23 of its 118 references that are in the map. First posted on arXiv 18 Oct 2021.

Builds on

23 of 118 references.

References · 23

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  5. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  6. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  7. 2018Deep contextualized word representationscited by 2,285
  8. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
  9. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  10. 2016Playing for Data: Ground Truth from Computer Gamescited by 816
  11. 2017Playing for Benchmarkscited by 169
  12. 2017Deep Learning for Sensor-based Activity Recognition: A Surveycited by 133
11 more
  1. 2020Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunitiescited by 125
  2. 2019U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Stagingcited by 87
  3. 2018Transfer learning for time series classificationcited by 60
  4. 2021Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Gamescited by 17
  5. 2020Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homescited by 14
  6. 2018Human Activity Recognition using Recurrent Neural Networkscited by 10
  7. 2019Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence Modelcited by 8
  8. 2019Online Human Activity Recognition Employing Hierarchical Hidden Markov Modelscited by 7
  9. 2019Different Approaches for Human Activity Recognition: A Surveycited by 7
  10. 2017Concurrent Activity Recognition with Multimodal CNN-LSTM Structurecited by 5
  11. 2021Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchycited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 8; 2025: 8; 2026: 2.

21 papers.

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  1. 2025Xiaomi MiMo-VL-Miloco Technical Reportcited by 15
  2. 2024Transfer Learning in Human Activity Recognition: A Surveycited by 14
  3. 2024Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)cited by 13
  4. 2021Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homescited by 12
  5. 2025Hierarchical and Multimodal Data for Daily Activity Understandingcited by 6
  6. 2025AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environmentscited by 4
  7. 2024Creation of AI-driven Smart Spaces for Enhanced Indoor Environments -- A Surveycited by 2
  8. 2023A Survey on Multi-Resident Activity Recognition in Smart Environmentscited by 1
  9. 2024Game of LLMs: Discovering Structural Constructs in Activities using Large Language Modelscited by 1
  10. 2024Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homescited by 1
  11. 2025MARAuder's Map: Motion-Aware Real-time Activity Recognition with Layout-Based Trajectoriescited by 1
  12. 2023On the recognition of the game type based on physiological signals and eye trackingcited by 0
9 more
  1. 2024Meta-Decomposition: Dynamic Segmentation Approach Selection in IoT-based Activity Recognitioncited by 0
  2. 2024An Automated Approach to Collecting and Labeling Time Series Data for Event Detection Using Elastic Node Hardwarecited by 0
  3. 2024Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognitioncited by 0
  4. 2025GNN-XAR: A Graph Neural Network for Explainable Activity Recognition in Smart Homescited by 0
  5. 2025DISCOVER: Identifying Patterns of Daily Living in Human Activities from Smart Home Datacited by 0
  6. 2025Evaluating BiLSTM and CNN+GRU Approaches for Human Activity Recognition Using WiFi CSI Datacited by 0
  7. 2025Plural Voices, Single Agent: Towards Inclusive AI in Multi-User Domestic Spacescited by 0
  8. 2026Evaluating Few-Shot Temporal Reasoning of LLMs for Human Activity Prediction in Smart Environmentscited by 0
  9. 2026LastAct: Trajectory-Guided Latest-Activity Localization for Real-Time Smart-Home Activity Recognitioncited by 0