Action Recognition for Privacy-Preserving Ambient Assisted Living
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 14976 LNCS, Page: 203-217
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
The care challenges posed by an increasing elderly population have made ambient assisted living a significant research focus. Computer vision-based technologies can monitor older adults’ daily activities in their homes, providing insights into their health and prolonging their capacity to live independently. However, despite the benefits of these technologies, their widespread adoption has been hampered due to privacy concerns. These concerns frequently stem from the need to stream user data to cloud servers for computation, posing a risk to user privacy. This study proposes a privacy-preserving method for activity recognition that enhances the accuracy of activity recognition locally, eliminating the need to stream user data to the cloud. The paper’s contributions are twofold: a Temporal Decoupling Graph Depthwise Separable Convolution Network (TD-GDSCN) to address the challenges of real-time performance and a data augmentation technique to prevent accuracy degradation in real-world environmental conditions. The experimental results show that the TD-GDSCN and data augmentation techniques outperform existing methods in addressing real-time performance and degradation challenges on the NTU-RGB+D 60 and NW-UCLA datasets.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85202301906&origin=inward; http://dx.doi.org/10.1007/978-3-031-67285-9_15; https://link.springer.com/10.1007/978-3-031-67285-9_15; https://dx.doi.org/10.1007/978-3-031-67285-9_15; https://link.springer.com/chapter/10.1007/978-3-031-67285-9_15
Springer Science and Business Media LLC
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