Real-Time Mobile Emotional Content Player Using Smartphone Camera-Based PPG Measurement

Lee, Haena; Kim, Dong Keun

doi:10.1155/2019/5820437

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…The measurement using a PPG/BVP sensor measures the blood flow and extracts the heartbeat signal from optical electronics by absorbing the infrared light of the cell and blood vessels. The method of measuring and applying bio-signals in this manner has the advantages of simplicity and ease of operation even with the newest smartphones [38].…”

Section: Emotion Analysis Methodsmentioning

confidence: 99%

The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals

Lee

Kim

2020

Sensors

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This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by bio-signal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals such as respiration (RSP) and heart rate variability (HRV) were acquired in an experiment from 53 participants when six basic emotion states were induced. Two RSP parameters were acquired from a chest-band respiration sensor, and five HRV parameters were acquired from a finger-clip blood volume pulse (BVP) sensor. A newly designed deep-learning model based on a convolutional neural network (CNN) was adopted for detecting the identification accuracy of individual emotions. Additionally, the signal combination of the acquired parameters was proposed to obtain high classification accuracy. Furthermore, a dominant factor influencing the accuracy was found by comparing the relativeness of the parameters, providing a basis for supporting the results of emotion classification. The users of this proposed model will soon be able to improve the emotion recognition model further based on CNN using multimodal physiological signals and their sensors.

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Section: Emotion Analysis Methodsmentioning

confidence: 99%

The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals

Lee

Kim

2020

Sensors

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“…Physiological cognitive load measurement has been majorly done using Electroencephalography(EEG) activity. As, EEG signal is noisy and a non‐stationary signal (Saitis et al, 2018), there is a need to recognize further accurate systems for identifying high cognitive load and stress factors experienced by our brain, which could correlate to subjective experiences; galvanic skin response (GSR), heart rate variability (HRV), breathing rate (BR), photoplethysmography (PPG; Reiner et al, 2019; Lee & Kim, 2019), electro dermal activity (EDA), and so on have been utilized to measure quantitative variations in cognitive load (Bianco et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Chirplet transform‐based machine‐learning approach towards classification of cognitive state change using galvanic skin response and photoplethysmography signals

et al. 2022

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Recent advances in the field of cognitive sciences have provided evidence to classify activities with different cognitive engagement levels. High cognitive load and sustained attention may predispose an individual to psychological and physical stress and may result in a lack of performance. Cognitive load monitoring of professionals in high precision fields has gathered significant attention in the current research scenario. The proposed work in continuity has tried to demonstrate the use of two non‐invasive physiological sensing modalities viz. galvanic skin response (GSR) and photoplethysmography (PPG) to elucidate effectiveness in picking up differences of two induced cognitive state categories based on machine learning and statistical methods. This paper demonstrates how GSR and PPG signals data can be effectively utilized to examine the shift in cognitive load. The present work proposes the use of general linear chirplet transform (GLCT) to evaluate the time‐frequency characteristic of GSR and PPG signals and utilize the statistical features in classification. Random Forest, Decision Tree, and k‐Nearest Neighbours demonstrated an accuracy of 92.13%, 88.0%, and 86.13% respectively on a dataset of 20 subjects against an optimized feature set, thus demonstrating the effectiveness of the proposed methodology for differentiating pre‐defined categories of cognitive load. The study shows the potential of GSR and PPG signals attributes and time‐frequency representation using GLCT to monitor cognitive load in real‐life conditions. The proposed work also indicates the possibility to extend the same to attention‐demanding fields such as aviation, medicine, and manufacturing for effective remediation of fatigue periods with increased cognitive demand, which, if sustained, may lead to cognitive decline in the long run.

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“…have made us enter an era of image information. The growth of this information and the development of computer hardware technology have made great progress in image detection [2,3]. As an important part of the new round of artificial intelligence revolution, image detection also has an increasingly important impact on our lives.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Image Detection System Based on Internet of Things and Cloud Computing

admin¹,

Algrnaodi²

2021

JISIoT

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Images are the most intuitive way for humans to perceive and obtain information, and they are one of the most important sources of information. With the development of information technology, the use of digital image processing methods to locate and identify targets is widely used, so it is particularly important to detect the targets of interest quickly and accurately in the image. The traditional image detection system has the problems of low detection accuracy, long time consumption, and poor stability. Therefore, this paper proposes the design and research of artificial intelligence image detection system based on Internet of Things and cloud computing. The system designed in this article mainly includes three links, namely: image processing analysis design link in cloud computing environment, image feature collection module design link, and image integration detection link. The main technologies used in image processing and analysis in the cloud computing environment are virtualization technology, distributed massive data storage, and distributed computing. In the image feature collection module, before the image is input to the neural network, it is necessary to perform preprocessing operations on the distorted image and perform perspective correction; then use the deep residual network in deep learning to extract features. Finally, there is the image integration detection link. First, the target category judgment and position correction are performed on the regions generated by the candidate region generation network, and then the integrated image detection is performed through the improved target detection method based on the frame difference method. Through simulation experiments, compared with the traditional image detection system, the speed advantage of the artificial intelligence image detection system designed in this paper is obvious in the case of a large increase in the number of images. On images at different pixel levels, the accuracy of the image detection system proposed in this paper is always higher than that of traditional image detection systems, and the CPU usage and memory usage are at a lower level. In addition, within three months, the stability is also at a relatively high level of 0.9.

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Real-Time Mobile Emotional Content Player Using Smartphone Camera-Based PPG Measurement

Cited by 3 publications

References 33 publications

The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals

The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals

Chirplet transform‐based machine‐learning approach towards classification of cognitive state change using galvanic skin response and photoplethysmography signals

Intelligent Image Detection System Based on Internet of Things and Cloud Computing

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