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28 December 2022 Recognizing human activities with attention mechanism on multi-source sensor data
Haixia Bi, Miquel Perello-Nieto, Emma Tonkin, Raul Santos-Rodriguez, Peter Flach, Ian Craddock
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Proceedings Volume 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022); 125064K (2022) https://doi.org/10.1117/12.2661819
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2022), 2022, Beijing, China
Abstract
A smart home equipped with a diversity of multimodal sensors is a meaningful setting for acquiring the health status of its residents and improving their well-being. In recent years, sensor-based activity recognition has received growing research attention. However, the multi-modal nature of these sensor platforms raises great challenges with respect to the data fusion of the different sensor sources. To solve this problem, we present an activity recognition approach incorporating attention mechanism in this paper. A Convolutional Neural Network-based training framework is developed to extract representative features for activities. Specifically, we design two attention modules-channel-wise and temporal-wise modules to capture the interdependencies between channel and temporal dimensions of its convolutional features. We evaluate the attention-based approach on a real activity recognition challenge dataset. Experiments justify that the attention network-based feature fusion can effectively improve the activity recognition performance.
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Haixia Bi, Miquel Perello-Nieto, Emma Tonkin, Raul Santos-Rodriguez, Peter Flach, and Ian Craddock "Recognizing human activities with attention mechanism on multi-source sensor data", Proc. SPIE 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022), 125064K (28 December 2022); https://doi.org/10.1117/12.2661819
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KEYWORDS
Sensors
Optical spheres
Convolution
Video
Cameras
Data fusion
Environmental sensing
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