ArvaNet
Deep Recurrent Architecture for PPG-Based Negative Mental-State Monitoring
Bibliographic Data
| ID | 22107387 |
|---|---|
| Authors | Hongbo Zhu (0000-0003-4467-5811, Shenyang Ligong University), Guangjie Han (0000-0002-6921-7369, Nanjing Agricultural University), Lei Shu (0000-0003-0505-9506, Nanjing Agricultural University), Hai Zhao (0000-0001-7290-0487, Northeastern University) |
| Year | 2021 |
| Volume | 8 |
| Issue | 1 |
| Pages | 179-190 |
| Publication date | 2021-02-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2020.2977715 |
| OpenAlex | W3011425517 |
| Language | EN |
| Citations received | 4 |
| References cited | 35 |
Depression and anxiety are a couple of pernicious mental states, which may affect lifestyle and quality and even become the primary causes of disability worldwide. Hence, daily monitoring of the mental states is significant for avoiding possible injury. Dynamics of the human blood vascular system convey significant information on recording the emotion and the mental state, which can be monitored via photoplethysmography (PPG). It is one of the best schemes with the advantages of nonintrusiveness and low cost. Conventional approaches for PPG signal analysis usually depend on handcrafted feature extraction and classification, thus resulting in a lack of feature discrimination and difficulties in generalization. In this article, we propose an attentive deep recurrent architecture called Arousal-valence Networks (ArvaNets), which benefits from graph convolutional networks and recurrent neural networks. Our approach overcomes the limitations of previous methods by automatically extracting the learnable spatial representations from a rigorous custom data set as semantic motifs to infer immediate emotions, which are mapped to a 2-D arousal-valence coordinate system. Finally, we exploit long short-term memory (LSTM) units to output the mental states by incorporating the temporal factor. During the entire inference, we propose a spatiotemporal attention mechanism based on correlation fractal dimensions (CFDs) and time-averaged wall shear stress (TAWSS) to capture and stress the key subtle motifs for performance optimization. Experimental results demonstrate the proposed architecture has enough competitiveness in the tasks of emotion and mental-state recognition for daily monitoring
Cognitive psychology · Computer vision · Convolutional neural network · Deep learning · Feature extraction · Inference · Photoplethysmogram · Computer Science · EEG and Brain-Computer Interfaces · Heart Rate Variability and Autonomic Control · Non-Invasive Vital Sign Monitoring · Psychology · Artificial Intelligence
| Citations per year | 1 |
|---|---|
| Citation span | 2022 - 2025 (4) |
| Highly cited | No |