A Novel Fuzzy Rough Nearest Neighbors Emotion Recognition Approach Based on Multimodal Wearable Biosensor Network

Zheng, Shupeng; Peng, Chenhui; Fang, Fang; Liu, Xiaochu

doi:10.1166/jmihi.2020.2922

Cited by 2 publications

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“…Moreover, there is a study that used wearable sensors to make emotion recognition more practical and flexible in a wide field. Zheng et al [ 21 ] constructed an emotion recognition approach based on a multimodal wearable biosensor network to facilitate emotion monitoring in daily life. The signals from the multimodal wearable biosensor were collected by measuring nodes and then transmitted to the sink node.…”

Section: Related Workmentioning

confidence: 99%

“…Ultimately, the driver’s real emotion that we aim for is not concealed emotion but emotion that is not fully revealed. Furthermore, most research uses bio-physiology signals for recognizing human emotions [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. The most commonly used bio-physiology signals are electroencephalogram (EEG), electrocardiogram (ECG), photoplethysmography (PPG) and electrodermal activity (EDA).…”

Section: Introductionmentioning

confidence: 99%

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DRER: Deep Learning–Based Driver’s Real Emotion Recognizer

Ryu

Jeong

et al. 2021

Sensors

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In intelligent vehicles, it is essential to monitor the driver’s condition; however, recognizing the driver’s emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monitor the driver’s emotional state. However, while driving, many factors are preventing the drivers from revealing the emotions on their faces. To address this problem, we propose a deep learning-based driver’s real emotion recognizer (DRER), which is a deep learning-based algorithm to recognize the drivers’ real emotions that cannot be completely identified based on their facial expressions. The proposed algorithm comprises of two models: (i) facial expression recognition model, which refers to the state-of-the-art convolutional neural network structure; and (ii) sensor fusion emotion recognition model, which fuses the recognized state of facial expressions with electrodermal activity, a bio-physiological signal representing electrical characteristics of the skin, in recognizing even the driver’s real emotional state. Hence, we categorized the driver’s emotion and conducted human-in-the-loop experiments to acquire the data. Experimental results show that the proposed fusing approach achieves 114% increase in accuracy compared to using only the facial expressions and 146% increase in accuracy compare to using only the electrodermal activity. In conclusion, our proposed method achieves 86.8% recognition accuracy in recognizing the driver’s induced emotion while driving situation.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DRER: Deep Learning–Based Driver’s Real Emotion Recognizer

Ryu

Jeong

et al. 2021

Sensors

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Driver Emotions Recognition Based on Improved Faster R-CNN and Neural Architectural Search Network

et al. 2022

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It is critical for intelligent vehicles to be capable of monitoring the health and well-being of the drivers they transport on a continuous basis. This is especially true in the case of autonomous vehicles. To address the issue, an automatic system is developed for driver’s real emotion recognizer (DRER) using deep learning. The emotional values of drivers in indoor vehicles are symmetrically mapped to image design in order to investigate the characteristics of abstract expressions, expression design principles, and an experimental evaluation is conducted based on existing research on the design of driver facial expressions for intelligent products. By substituting a custom-created CNN features learning block with the base 11 layers CNN model in this paper for the development of an improved faster R-CNN face detector that detects the driver’s face at a high frame per second (FPS). Transfer learning is performed in the NasNet large CNN model in order to recognize the driver’s various emotions. Additionally, a custom driver emotion recognition image dataset is being developed as part of this research task. The proposed model, which is a combination of an improved faster R-CNN and transfer learning in NasNet-Large CNN architecture for DER based on facial images, enables greater accuracy than previously possible for DER based on facial images. The proposed model outperforms some recently updated state-of-the-art techniques in terms of accuracy. The proposed model achieved the following accuracy on various benchmark datasets: JAFFE 98.48%, CK+ 99.73%, FER-2013 99.95%, AffectNet 95.28%, and 99.15% on a custom-developed dataset.

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A Novel Fuzzy Rough Nearest Neighbors Emotion Recognition Approach Based on Multimodal Wearable Biosensor Network

Cited by 2 publications

References 0 publications

DRER: Deep Learning–Based Driver’s Real Emotion Recognizer

DRER: Deep Learning–Based Driver’s Real Emotion Recognizer

Driver Emotions Recognition Based on Improved Faster R-CNN and Neural Architectural Search Network

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