Improvement of emotional healthcare system with stress detection from ECG signal

Tivatansakul, Somchanok; Ohkura, Michiko

doi:10.1109/embc.2015.7319953

Cited by 16 publications

(9 citation statements)

References 3 publications

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“…Emotion recognition by facial expression was integrated with stress detection using ECG signal [31], which increased the efficiency and effectiveness of the system. A small and lightweight sensor named RF-ECG was used to record the real-time ECG signals with 204 Hz sampling rate.…”

Section: Cstress Detection Using Ecgmentioning

confidence: 99%

A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques

2021

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Stress is an escalated psycho-physiological state of the human body emerging in response to a challenging event or a demanding condition. Environmental factors that trigger stress are called stressors. In case of prolonged exposure to multiple stressors impacting simultaneously, a person's mental and physical health can be adversely affected which can further lead to chronic health issues. To prevent stress-related issues, it is necessary to detect them in the nascent stages which are possible only by continuous monitoring of stress. Wearable devices promise real-time and continuous data collection, which helps in personal stress monitoring. In this paper, a comprehensive review has been presented, which focuses on stress detection using wearable sensors and applied machine learning techniques. This paper investigates the stress detection approaches adopted in accordance with the sensory devices such as wearable sensors, Electrocardiogram (ECG), Electroencephalography (EEG), and Photoplethysmography (PPG), and also depending on various environments like during driving, studying, and working. The stressors, techniques, results, advantages, limitations, and issues for each study are highlighted and expected to provide a path for future research studies. Also, a multimodal stress detection system using a wearable sensor-based deep learning technique has been proposed at the end.

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Section: Cstress Detection Using Ecgmentioning

confidence: 99%

A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques

2021

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“…The ECG is one of the most extensively used signals in stress detection research [30,31], because it directly reflects the activity of the heart, which in turn is affected by Autonomic Nervous System (ANS) changes. The characteristic peaks of the ECG are denoted by the letters P, Q, R, S, and T. The R-peak (i.e., the most distinctive peak) is considered crucial, and most analyses exploit the distribution of the time elapsed between two successive R-peaks-usually called RR intervals (RRI) or interbeat intervals (IBI) [32]. • Photoplethysmography (PPG): PPG is a simple optical technique used to detect volumetric changes in blood in peripheral circulation [33].…”

Section: Biosignalsmentioning

confidence: 99%

AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring

Mitro,Nikos

Argyri

Pavlopoulos

et al. 2023

Sensors

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This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers’ physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%.

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“…According to the HeartMath Institute, the dynamic, continuous, and bidirectional communication of both organs affects one’s perception, emotion, intuition, and general health [ 13 ]. Hence, detecting the cardiac rhythm for emotion recognition purposes based on autonomic innervation is necessary in healthcare as a preventive measure towards negative emotions such as stress [ 18 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…The authors of [ 7 , 18 ] proposed a new healthcare system that focuses on emotional wellbeing. The system consists of physiological sensors (ECG and EEG) to measure and detect emotions.…”

Section: Application Of Emotion Recognition System In Healthcarementioning

confidence: 99%

Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review

Hasnul

Aziz

Alelyani

et al. 2021

Sensors

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Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

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Improvement of emotional healthcare system with stress detection from ECG signal

Cited by 16 publications

References 3 publications

A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques

A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques

AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring

Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review

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