Physical Activity Recognition with Statistical-Deep Fusion Model Using Multiple Sensory Data for Smart Health
Huynh-The T. Hua C.-H. Tu N.A. Kim D.-S.
1 February 2021Institute of Electrical and Electronics Engineers Inc.
IEEE Internet of Things Journal
2021#8Issue 31533 - 1543 pp.
Nowadays, enhancing the living standard with smart healthcare via the Internet of Things 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 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 fusion with conventional handcrafted features, we learn an intermediate fusion framework of human activity recognition (HAR). According to transforming the raw signal value to pixel intensity value, segmentation data acquired from a multisensor system are encoded to an activity image for deep model learning. Formulated by several novel residual triple convolutional blocks, the proposed DCNN allows extracting multiscale spatiotemporal signal-level and sensor-level correlations simultaneously from the activity image. In the fusion model, the hybrid feature merged from the handcrafted and deep features is learned by a multiclass support vector machine (SVM) classifier. Based on several experiments of performance evaluation, our fusion approach for activity recognition has achieved the accuracy over 96.0% on three public benchmark data sets, including Daily and Sport Activities, Daily Life Activities, and RealWorld. Furthermore, the method outperforms several state-of-the-art HAR approaches and demonstrates the superiority of the proposed intermediate fusion model in multisensor systems.
Deep learning , intermediate fusion , physical activity (PA) recognition , wearable sensor system
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Department of IT Convergence Engineering, The ICT Convergence Research Center, Kumoh National Institute of Technology, Gumi, 39177, South Korea
Department of Computer Science and Engineering, Kyung Hee University, Yongin, 446-701, South Korea
School of Engineering and Digital Sciences, Nazarbayev University, Astana, 010000, Kazakhstan
Department of IT Convergence Engineering
Department of Computer Science and Engineering
School of Engineering and Digital Sciences
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