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• W4289656146 abstract "With the popularity and development of Internet of Things (IoT) technology, human activity recognition using IoT devices such as wearable sensors can be implemented for various applications. Due to the complexity of activity recognition, multiple homogeneous or heterogeneous sensors are used to obtain excessive information in most wearable activity recognition systems. However, the increased number of sensors and the way of multichannel signal data bring huge challenges to human activity recognition tasks. How to select suitable sensor channels to balance the computational complexity and recognition accuracy has become a major issue. In this article, we extend the sparse group Lasso mechanism to human activity recognition tasks and propose a hybrid attention-based multi-sensor pruning and feature selection deep neural network, called HAP-DNN. This architecture is able to further perform feature selection on the basis of sensor pruning. HAP-DNN consists of three detachable modules: 1) a feature compression & reconstruction module for sensor feature information fusion and restoration; 2) a feature weight calculation module for calculating sensor channel weights and feature weights; and 3) a learning module for classification, which can be regarded as a filter feature selection method. Four public activity recognition data sets are used to verify our proposed architecture, and the experimental results show that HAP-DNN achieves the best classification performance with the least number of retained feature channels." @default.
• W4289656146 created "2022-08-03" @default.
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• W4289656146 date "2022-12-15" @default.
• W4289656146 modified "2023-10-11" @default.
• W4289656146 title "A Hybrid Attention-Based Deep Neural Network for Simultaneous Multi-Sensor Pruning and Human Activity Recognition" @default.
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• W4289656146 doi "https://doi.org/10.1109/jiot.2022.3196170" @default.
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