mlsanityData to 14 Oct 2026

Action Recognition in Real-World Ambient Assisted Living Environment

arXiv 2503.23214 ↗First posted 29 Mar 2025cs.CV cs.AIcited by 2

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Abstract (arXiv)

The growing ageing population and their preference to maintain independence by living in their own homes require proactive strategies to ensure safety and support. Ambient Assisted Living (AAL) technologies have emerged to facilitate ageing in place by offering continuous monitoring and assistance within the home. Within AAL technologies, action recognition plays a crucial role in interpreting human activities and detecting incidents like falls, mobility decline, or unusual behaviours that may signal worsening health conditions. However, action recognition in practical AAL applications presents challenges, including occlusions, noisy data, and the need for real-time performance. While advancements have been made in accuracy, robustness to noise, and computation efficiency, achieving a balance among them all remains a challenge. To address this challenge, this paper introduces the Robust and Efficient Temporal Convolution network (RE-TCN), which comprises three main elements: Adaptive Temporal Weighting (ATW), Depthwise Separable Convolutions (DSC), and data augmentation techniques. These elements aim to enhance the model's accuracy, robustness against noise and occlusion, and computational efficiency within real-world AAL contexts. RE-TCN outperforms existing models in terms of accuracy, noise and occlusion robustness, and has been validated on four benchmark datasets: NTU RGB+D 60, Northwestern-UCLA, SHREC'17, and DHG-14/28. The code is publicly available at: https://github.com/Gbouna/RE-TCN

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 29 of its 74 references that are in the map. First posted on arXiv 29 Mar 2025.

Builds on

29 of 74 references.

References · 29

  1. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  2. 2018TSM: Temporal Shift Module for Efficient Video Understandingcited by 856
  3. 2016NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysiscited by 686
  4. 2020Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognitioncited by 343
  5. 2021Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognitioncited by 322
  6. 2019Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognitioncited by 228
  7. 2019An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognitioncited by 151
  8. 2019Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognitioncited by 135
  9. 2020BlazePose: On-device Real-time Body Pose trackingcited by 113
  10. 2019Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networkscited by 103
  11. 2022Back to MLP: A Simple Baseline for Human Motion Predictioncited by 100
  12. 2020Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognitioncited by 91
17 more
  1. 2021Constructing Stronger and Faster Baselines for Skeleton-based Action Recognitioncited by 87
  2. 2022PYSKL: Towards Good Practices for Skeleton Action Recognitioncited by 83
  3. 2020Spatial Temporal Transformer Network for Skeleton-based Action Recognitioncited by 77
  4. 2019Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searchingcited by 70
  5. 2019Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Predictioncited by 50
  6. 2018Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learningcited by 47
  7. 2019A neural network based on SPD manifold learning for skeleton-based hand gesture recognitioncited by 46
  8. 2015Differential Recurrent Neural Networks for Action Recognitioncited by 39
  9. 2020Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognitioncited by 36
  10. 2020Feedback Graph Convolutional Network for Skeleton-based Action Recognitioncited by 22
  11. 2020Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 18
  12. 2022Skeleton-based Action Recognition via Temporal-Channel Aggregationcited by 13
  13. 2019Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletonscited by 12
  14. 2021RF-Based Human Activity Recognition Using Signal Adapted Convolutional Neural Networkcited by 11
  15. 2020Predictively Encoded Graph Convolutional Network for Noise-Robust Skeleton-based Action Recognitioncited by 8
  16. 2022Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video Surveillancecited by 6
  17. 2023HD-GCN:A Hybrid Diffusion Graph Convolutional Networkcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Enhanced Sparse Point Cloud Data Processing for Privacy-aware Human Action Recognitioncited by 1
  2. 2026Privacy-Preserving Industrial Ergonomics: mmWave-Based Automated REBA Scoring and Pose Estimationcited by 0