Action Recognition in Real-World Ambient Assisted Living Environment
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 w...
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Tsinghua University Press
2025-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2025.9020003 |
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| author | Vincent Gbouna Zakka Zhuangzhuang Dai Luis J. Manso |
| author_facet | Vincent Gbouna Zakka Zhuangzhuang Dai Luis J. Manso |
| author_sort | Vincent Gbouna Zakka |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a8e03c7f8f324fb49cb8b19016d6c60c |
| institution | Matheson Library |
| issn | 2096-0654 2097-406X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-a8e03c7f8f324fb49cb8b19016d6c60c2025-07-25T08:19:33ZengTsinghua University PressBig Data Mining and Analytics2096-06542097-406X2025-06-018491493210.26599/BDMA.2025.9020003Action Recognition in Real-World Ambient Assisted Living EnvironmentVincent Gbouna Zakka0https://orcid.org/0000-0001-9360-0985Zhuangzhuang Dai1https://orcid.org/0000-0002-6098-115XLuis J. Manso2https://orcid.org/0000-0003-2616-1120School of Computer Science and Digital Technologies, Aston University, Birmingham, B4 7ET, UKSchool of Computer Science and Digital Technologies, Aston University, Birmingham, B4 7ET, UKApplied Artificial Intelligence and Robotics Department, Aston University, Birmingham, B4 7ET, UKThe 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.https://www.sciopen.com/article/10.26599/BDMA.2025.9020003ambient assisted living (aal)action recognitionocclusion robustnoise robustcomputational efficiency |
| spellingShingle | Vincent Gbouna Zakka Zhuangzhuang Dai Luis J. Manso Action Recognition in Real-World Ambient Assisted Living Environment Big Data Mining and Analytics ambient assisted living (aal) action recognition occlusion robust noise robust computational efficiency |
| title | Action Recognition in Real-World Ambient Assisted Living Environment |
| title_full | Action Recognition in Real-World Ambient Assisted Living Environment |
| title_fullStr | Action Recognition in Real-World Ambient Assisted Living Environment |
| title_full_unstemmed | Action Recognition in Real-World Ambient Assisted Living Environment |
| title_short | Action Recognition in Real-World Ambient Assisted Living Environment |
| title_sort | action recognition in real world ambient assisted living environment |
| topic | ambient assisted living (aal) action recognition occlusion robust noise robust computational efficiency |
| url | https://www.sciopen.com/article/10.26599/BDMA.2025.9020003 |
| work_keys_str_mv | AT vincentgbounazakka actionrecognitioninrealworldambientassistedlivingenvironment AT zhuangzhuangdai actionrecognitioninrealworldambientassistedlivingenvironment AT luisjmanso actionrecognitioninrealworldambientassistedlivingenvironment |