kumoh national institute of technology
Networked Systems Lab.

Thien Huynh-The, Hua, Cam-Hao; Anh Tu, Nguyen; Kim, Dong-Seong, "Physical Activity Recognition with Statistical-Deep Fusion Model using Multiple Sensory Data for Smart Health" will be published IEEE IoT Journal
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Date : 2020-07-26
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Nowadays, enhancing the living standard with smart healthcare via Internet of Thing is one of the most critical goals of smart cities, in which Artificial Intelligence plays as the core technology. Many smart services, deployed according to wearable sensor-based physical activity (PA) recognition, have been able to early detect unhealthy daily behaviors and further medical risks. Numerous approaches have studied shallow handcrafted features coupled with traditional machine learning (ML) techniques, which find it difficult to model real-world activities. In this work, by revealing deep features from Deep Convolutional Neural Networks (DCNNs) in the fusion with conventional handcrafted features, we learn an intermediate fusion framework of human activity recognition (HAR). According to transforming raw signal value to pixel intensity value, a segmentation data acquired from a multi-sensor system is encoded to an