Emotion-Aware Connected Healthcare Big Data Towards 5G

Hossain, M. Shamim; Muhammad, Ghulam

doi:10.1109/jiot.2017.2772959

Cited by 236 publications

(117 citation statements)

References 29 publications

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“…The emerging 5G technology helps the implementation of big data-oriented wireless technologies. Hossain et al [1] proposed a framework using 5G technology for personalized and seamless emotion-aware healthcare services. The network architecture affects the quality of the received data, and a reasonably flexible architecture can use edge computing [14] to speed up the computational response.…”

Section: A Affective Computing In the Iotmentioning

confidence: 99%

“…Due to the massive data and powerful computational capacity, the fast growth of Internet of Things (IoT) technologies helps to realize the possibility of real-time human emotion detection or perception in different scenarios. There have been many novel studies combining the IoT and affective computing to provide various emotion detection frameworks for different applications, such as healthcare services [1], battlefield environments [2] and smart homes [3]. These studies treat emotion detection as a part of the overall framework and tend to explain the feasibility of communication and interaction processes.…”

Section: Introductionmentioning

confidence: 99%

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Multistep Deep System for Multimodal Emotion Detection With Invalid Data in the Internet of Things

Xie

Lv³

et al. 2020

IEEE Access

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The Internet of Things (IoT) technologies such as interconnection and edge computing help emotion recognition to be applied in healthcare, smart education, etc. However, the acquisition and transmission processes may have some situations, such as lost signals and serious interference noise caused by motion, which affect the quality of the received data and limit the performance of IoT emotion detection. We collectively refer to these as invalid data. A multi-step deep (MSD) system is proposed to reliably detect multimodal emotion by the collected records containing invalid data. Semantic compatibility and continuity are utilized to filter out the invalid data. The feature from invalid modal data is replaced through the imputation method to compensate for the impact of invalid data on emotion detection. In this way, the proposed system can automatically process invalid data and improve the recognition performance. Furthermore, considering the spatiotemporal information, the features of video and physiological signals are extracted by specific deep neural networks in the MSD system. The simulation experiments are conducted on a public multimodal database, and the performance of the MSD system measured by the unweighted average recall is better than that of the traditional system. The promising results observed in the experiments verify the potential influence of the proposed system in practical IoT applications. INDEX TERMS Internet of Things, multimodal emotion detection, invalid data, multi-step deep (MSD) system, deep neural networks

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Section: A Affective Computing In the Iotmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multistep Deep System for Multimodal Emotion Detection With Invalid Data in the Internet of Things

Xie

Lv³

et al. 2020

IEEE Access

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“…This paper deal with epileptical patients monitoring Health-IoT system, where along with users, numbers of experts are connected in a network and user may have the provision of second opinion too [8]. For maintaining electronics health record in an IoT based network a brief discussion is given by authors [9]. To save patients data from hacker or from being misused information has been mentioned by authors [10].…”

Section: Related Workmentioning

confidence: 99%

Monitoring of Epileptical Patients Using Cloud-Enabled Health-IoT System

Gupta¹,

Chakraborty²,

Gupta³

2019

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The health Internet of Things (IoT) lays the basis for emergency care for epileptic patients. The security of data transmission in the network calls for a robust monitoring technique. This paper proposes a monitoring model for epileptic patients, using a cloud-based health IoT system. To ensure the data security, watermarking was carried out through discrete wavelet transform-singular value decomposition (DWT-SVD), followed by short time Fourier transform (STFT). The proposed watermarking scheme, which is based on STFT and DWT-SVD, was verified on electroencephalography (EEG) data of class Z and class S. The results show that our scheme achieved a good watermarking performance, with a peak signal-to-noise ratio (PSNR) of 35.25 and a signal-to-noise ratio (SNR) of 31.32.

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“…The performance of this work is then analysed and the results are discussed. An emotion aware connected healthcare big data towards 5G is presented in [4]. The image and speech signals of the patients are captured from the Internet of Things (IoT) and are passed as input to the emotion detection module.…”

Section: Review Of Literaturementioning

confidence: 99%

Big Data Based Diabetes and Heart Disease Prediction System by Employing Supervised Learning Algorithm

Christy*,

Balamurugan

2019

IJITEE

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Healthcare systems generate bytes and bytes of data and the data growth is exponential. The voluminous data can be analysed effectively, only when the data organization is efficient. Additionally, data retrieval must also be made simpler, such that the healthcare professional can compare and contrast the test sample with the database of health records. This makes it possible to achieve better disease prediction and this work presents a big data based disease prediction system with the help of supervised learning. The proposed approach clusters the related health records, based on every medical attribute followed by which the disease is predicted by SVM classifier. The performance of the proposed disease prediction system is observed to be satisfactory in terms of accuracy, precision, recall, F-measure, while consuming reasonable period of time.

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Emotion-Aware Connected Healthcare Big Data Towards 5G

Cited by 236 publications

References 29 publications

Multistep Deep System for Multimodal Emotion Detection With Invalid Data in the Internet of Things

Multistep Deep System for Multimodal Emotion Detection With Invalid Data in the Internet of Things

Monitoring of Epileptical Patients Using Cloud-Enabled Health-IoT System

Big Data Based Diabetes and Heart Disease Prediction System by Employing Supervised Learning Algorithm

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