A smart wearable system for short-term cardiovascular risk assessment with emotional dynamics

Akbulut, Fatma Patlar; Akan, Aydın

doi:10.1016/j.measurement.2018.06.050

Cited by 38 publications

(16 citation statements)

References 24 publications

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“…In addition to examining the patient's biosignals, the use of PA results and stress levels inferred from the emotional state analysis achieved higher performance in risk estimation. The highest accuracy for determining short-term health status was 96% in the study [80] …”

Section: Big Data Technology For Cardiovascular Care Using Wearable D...mentioning

confidence: 71%

Wearable sensing, big data technology for cardiovascular healthcare: current status and future prospective

Miao

Liu

et al. 2022

Chinese Medical Journal

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Wearable technology, which can continuously and remotely monitor physiological and behavioral parameters by incorporated into clothing or worn as an accessory, introduces a new era for ubiquitous health care. With big data technology, wearable data can be analyzed to help long-term cardiovascular care. This review summarizes the recent developments of wearable technology related to cardiovascular care, highlighting the most common wearable devices and their accuracy. We also examined the application of these devices in cardiovascular healthcare, such as the early detection of arrhythmias, measuring blood pressure, and detecting prevalent diabetes. We provide an overview of the challenges that hinder the widespread application of wearable devices, such as inadequate device accuracy, data redundancy, concerns associated with data security, and lack of meaningful criteria, and offer potential solutions. Finally, the future research direction for cardiovascular care using wearable devices is discussed.

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Section: Big Data Technology For Cardiovascular Care Using Wearable D...mentioning

confidence: 71%

Wearable sensing, big data technology for cardiovascular healthcare: current status and future prospective

Miao

Liu

et al. 2022

Chinese Medical Journal

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“…Where specifies the 's average value G4 (case 6): In G4, case '6' is used (leucophores cells operator) as a global search for an arbitrary solution via reflecting the incoming light, which is evaluated in equation (12). The CFOA pseudo-code is evinced in fig 3. The outcome of the training contains '2' classes centered upon the heart condition of the patient as i) normal and ii) abnormal.…”

Section: G2 (Case 3 and Case 4)mentioning

confidence: 99%

“…Besides that, due to augmented hazard factors, like hypertension, diabetes, elevated blood fat, and weight gain caused amid menopause can make women suffer a heart attack. [12]. Studies conducted on heart failure patients show that around 30 percent of patients had been readmitted as a minimum of once within the timeframe of 90 days.…”

Section: Introductionmentioning

confidence: 99%

An Efficient IoT-Based Patient Monitoring and Heart Disease Prediction System Using Deep Learning Modified Neural Network

Sarmah¹

2020

IEEE Access

149

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The leading causes of death worldwide are chronic illnesses suchlike diabetes, Heart Disease (HD), cancer as well as chronic respiratory malady. It is remarkably intricate to diagnose HD with disparate symptoms or features. With the augmentation in popularity of smart wearable gadgets, a chance to render an Internet of Things (IoT) solution has turned out to be more. Unfortunately, the survival rates are low for the people suffering from sudden heart attacks. Consequently, a patient monitoring scheme intended for heart patients utilizing IoT centered Deep Learning Modified Neural Network (DLMNN) is proposed to assist in the HD diagnosis, and medication is given accordingly. This proposed technique is executed via '3' steps: I) Authentication, ii) Encryption, and iii) Classification. First, by utilizing the substitution cipher (SC) together with the SHA-512, the heart patient of the specific hospital is authenticated. Subsequently, the wearable IoT sensor device, which is fixed to the patient's body, concurrently transmits the sensor data to the cloud. This sensor data is encrypted and securely transmitted to the cloud utilizing the PDH-AES technique. After that, the encrypted data is finally decrypted, and by employing the DLMNN classifier, the classification is done. The classified outcomes comprise '2'types of data: i) normal and ii) abnormal. It denotes the patient's heart condition and if the outcome is abnormal, an alert text is passed to the physician for treating the patient. The investigational outcomes are estimated and the DLMNN for HD diagnosis shows improvement as compared to existing algorithms. Additionally, the proposed PDH-AES used in support of secure data transmission results in the highest level of security i.e. 95.87%, and it is achieved in the lowest time for encryption along with decryption when weighted against the existent AES. INDEX TERMS Disease prediction, Healthcare Monitoring System, Internet of Things (IoT), Advanced Encryption Standard (AES), Modified Huffman Algorithm (MHA), Deep Learning Modified Neural Network (DLMNN), and Cuttlefish Optimization Algorithm (CFOA).

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“…Today’s world benefits from automatic emotion recognition systems in different domains [ 6 ] such as patient care, medical diagnosis, education, video gaming, automotive assistance, recruiting personnel for companies, fraud detection for finance, etc.. For instance, emotion recognition systems can play a significant role in healthcare because the patient’s diagnosis and the recovery period can be understood and managed with higher accuracy [ 1 ]. In addition, it is utilized in tough follow-up processes of depression and anxiety patients [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of facial emotion expression in eating occasions using deep learning

Yıldırım

Akbulut

Çatal

2023

Multimed Tools Appl

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Eating is experienced as an emotional social activity in any culture. There are factors that influence the emotions felt during food consumption. The emotion felt while eating has a significant impact on our lives and affects different health conditions such as obesity. In addition, investigating the emotion during food consumption is considered a multidisciplinary problem ranging from neuroscience to anatomy. In this study, we focus on evaluating the emotional experience of different participants during eating activities and aim to analyze them automatically using deep learning models. We propose a facial expression-based prediction model to eliminate user bias in questionnaire-based assessment systems and to minimize false entries to the system. We measured the neural, behavioral, and physical manifestations of emotions with a mobile app and recognize emotional experiences from facial expressions. In this research, we used three different situations to test whether there could be any factor other than the food that could affect a person’s mood. We asked users to watch videos, listen to music or do nothing while eating. This way we found out that not only food but also external factors play a role in emotional change. We employed three Convolutional Neural Network (CNN) architectures, fine-tuned VGG16, and Deepface to recognize emotional responses during eating. The experimental results demonstrated that the fine-tuned VGG16 provides remarkable results with an overall accuracy of 77.68% for recognizing the four emotions. This system is an alternative to today’s survey-based restaurant and food evaluation systems.

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A smart wearable system for short-term cardiovascular risk assessment with emotional dynamics

Cited by 38 publications

References 24 publications

Wearable sensing, big data technology for cardiovascular healthcare: current status and future prospective

Wearable sensing, big data technology for cardiovascular healthcare: current status and future prospective

An Efficient IoT-Based Patient Monitoring and Heart Disease Prediction System Using Deep Learning Modified Neural Network

Analysis of facial emotion expression in eating occasions using deep learning

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