mlsanityData to 7 Oct 2026

Deep Learning for Sensor-based Activity Recognition: A Survey

arXiv 1707.03502 ↗First posted 12 Jul 2017cs.CV cs.AI cs.LGcited by 133

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Abstract (arXiv)

Sensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.

Key facts. Cited by 133 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jul 2017.

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Citing papers per year: 2021: 25; 2022: 31; 2023: 32; 2024: 17; 2025: 14; 2026: 14.

133 papers.

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  1. 2021Adaptive Inference through Early-Exit Networks: Design, Challenges and Directionscited by 74
  2. 2023FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learningcited by 70
  3. 2023Deep Learning for Time Series Classification and Extrinsic Regression: A Current Surveycited by 68
  4. 2021Deep Gait Recognition: A Surveycited by 54
  5. 2021Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advancescited by 54
  6. 2023LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Versioncited by 49
  7. 2021Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signalscited by 41
  8. 2022What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?cited by 39
  9. 2022MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcarecited by 36
  10. 2023EgoDistill: Egocentric Head Motion Distillation for Efficient Video Understandingcited by 32
  11. 2021A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparisoncited by 31
  12. 2022Out-of-Distribution Representation Learning for Time Series Classificationcited by 30
38 more
  1. 2022Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognitioncited by 28
  2. 2023DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalizationcited by 24
  3. 2022Graph Anomaly Detection with Graph Neural Networks: Current Status and Challengescited by 23
  4. 2021Cross-domain Activity Recognition via Substructural Optimal Transportcited by 21
  5. 2021Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysiscited by 21
  6. 2021Meta-HAR: Federated Representation Learning for Human Activity Recognitioncited by 21
  7. 2021A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learningcited by 21
  8. 2022Domain Generalization for Activity Recognition via Adaptive Feature Fusioncited by 16
  9. 2023Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognitioncited by 15
  10. 2022Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensionscited by 14
  11. 2024Initial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognitioncited by 12
  12. 2021An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligencecited by 11
  13. 2021Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognitioncited by 10
  14. 2022Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospectscited by 10
  15. 2023Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis Using Wearablescited by 10
  16. 2023Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcitycited by 9
  17. 2024Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Reviewcited by 9
  18. 2024Using Large Language Models to Compare Explainable Models for Smart Home Human Activity Recognitioncited by 9
  19. 2022Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognitioncited by 8
  20. 2022A Close Look into Human Activity Recognition Models using Deep Learningcited by 8
  21. 2021Knowing when we do not know: Bayesian continual learning for sensing-based analysis taskscited by 7
  22. 2022SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligencecited by 7
  23. 2022End-to-End Signal Classification in Signed Cumulative Distribution Transform Spacecited by 7
  24. 2022Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasetscited by 7
  25. 2022TrafficCAM: A Versatile Dataset for Traffic Flow Segmentationcited by 7
  26. 2021Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methodscited by 6
  27. 2022CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulatorcited by 6
  28. 2023Worker Activity Recognition in Manufacturing Line Using Near-body Electric Fieldcited by 6
  29. 2022Efficient Deep Clustering of Human Activities and How to Improve Evaluationcited by 5
  30. 2024Structural Knowledge Informed Continual Multivariate Time Series Forecastingcited by 5
  31. 2024Generalizable Sensor-Based Activity Recognition via Categorical Concept Invariant Learningcited by 5
  32. 2022YONO: Modeling Multiple Heterogeneous Neural Networks on Microcontrollerscited by 4
  33. 2023A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentationcited by 4
  34. 2024Explaining Time Series Classification Predictions via Causal Attributionscited by 4
  35. 2021Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphonescited by 3
  36. 2022Benchmarking Conventional Vision Models on Neuromorphic Fall Detection and Action Recognition Datasetcited by 3
  37. 2022Two ways to make your robot proactive: reasoning about human intentions, or reasoning about possible futurescited by 3
  38. 2022BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognitioncited by 3

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