Daily Stress and Mood Recognition System Using Deep Learning and Fuzzy Clustering for Promoting Better Well-Being

Lawanot, Worawat; Inoue, Masahiro; Yokemura, Taketoshi; Mongkolnam, Pornchai; Nukoolkit, Chakarida

doi:10.1109/icce.2019.8661932

Cited by 12 publications

(8 citation statements)

References 17 publications

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“…Virtual Reality technology to monitor stress is proposed in [15] for better health. A non-invasive stress monitoring method is proposed in [16]. A stress monitoring system in students during classes is proposed in [17] for better classroom experience.…”

Section: A Related Prior Researchmentioning

confidence: 99%

SaYoPillow: A Blockchain-Enabled, Privacy-Assured Framework for Stress Detection, Prediction and Control Considering Sleeping Habits in the IoMT

Rachakonda,

Bapatla,

Mohanty

et al. 2020

Preprint

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Considering today's lifestyle, people just sleep forgetting the benefits it provides to the human body. The reasons for not having a productive sleep could be many. Smart-Yoga Pillow (SaYoPillow) is envisioned as a device that may help in recognizing the importance of a good quality sleep to alleviate stress while establishing a measurable relationship between stress and sleeping habits. A system that analyzes the sleeping habits by continuously monitoring the physiological changes that occur during rapid eye movement (REM) and non-rapid eye movement (NREM) stages of sleep is proposed in the current work. In addition to the physiological parameter changes, factors such as sleep duration, snoring range, eye movement, and limb movements are also monitored. The SaYoPillow system is processed at the edge level with the storage being at the cloud. Not having to compromise the user's privacy, SaYoPillow proposes secure data transmission for both uploading and retrieving, and secure storage and communications as an attempt to reduce malicious attacks on healthcare. A user interface is provided for the user to control data accessibility and visibility. SaYoPillow has 96% accuracy which is close to other existing research works. However, SaYoPillow is the only work with security features as well as only work that considers sleeping habits for stress.

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Section: A Related Prior Researchmentioning

confidence: 99%

SaYoPillow: A Blockchain-Enabled, Privacy-Assured Framework for Stress Detection, Prediction and Control Considering Sleeping Habits in the IoMT

Rachakonda,

Bapatla,

Mohanty

et al. 2020

Preprint

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“…de los usuarios, y algunos algoritmos de inteligencia artificial como SVM y modelos de redes neuronales convolusionales. De estos trabajos destacan: i) la detección de emociones y la modificación del entorno para mejorar el estado emocional de usuario usando dispositivos IoT [24,26,34,36]; y, ii) La detección de emociones y estrés por medio de dispositivos IoT analizando rasgos faciales o patrones de movimiento del usuario [20,27].…”

Section: Discusión Y Conclusionesunclassified

“…Detección de emociones usando WoTVariable usada para la detecciónTécnica de procesamiento Referencia Patrón de movimiento, variables ambientales: Temperatura, Luz, Sonido, Encuesta PANAS Máquina de soporte vectorial SVM[20] …”

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Prevención del síndrome de Burnout mediante programación neurolingüística soportada por la Web de las Cosas: Mapeo sistemático

2020

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En esta investigación, mediante la exploración de la literatura, se realizó un mapeo sistemático sobre la aplicación de técnicas de programación neurolingüística (PNL) soportadas en la Web de las Cosas (WoT) para prevenir el síndrome de Burnout. Dicho síndrome es un tipo de estrés laboral que causa agotamiento físico, mental y emocional, generando una incapacidad para trabajar, dado que es un proceso paulatino en el cual el trabajador pierde interés por sus tareas, carece de sentido de responsabilidad y puede generar profundas depresiones. En los estudios encontrados se destaca el uso de la WoT para la detección de emociones y estrés laboral, para ello sobresale el uso de sensores capaces de medir Respuesta galvánica de la piel GSR, Frecuencia cardiaca HR, Fotopletismografía PPG, Electrocardiograma ECG, Cámaras, Micrófonos y Microprocesadores de bajo costo, así como la utilización de Inteligencia Artificial para procesar estos datos, entre las técnicas y los algoritmos más usados destacan Máquinas de Vectores de Soporte SVM, K-vecino más cercano y clasificador Naive Bayes. En los trabajos en los que se detectan emociones o estrés laboral son muy pocos los que intentan alterar el entorno mental o ambiental del usuario para llevarlo a una emoción positiva o disminuir el estrés. Se evidenció la posibilidad de la utilización de técnicas de PNL en la prevención del síndrome de Burnout. Sin embargo, no se encontró ningún trabajo que relacionara la WoT como soporte a las técnicas PNL para prevenir el síndrome de Burnout, lo cual se considera como una oportunidad de investigación en estos campos.

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“…Over many decades, a rich and mature field of research has emerged, with thousands of psychological studies that conceptually and empirically study worker well-being constructs such as job satisfaction (Judge et al 2017) and engagement (Macey and Schneider 2008;Purcell 2014). More recently, researchers from outside the psychological sciences have started to embrace the topic, including economics (Bryson et al 2013;Golden and Wiens-Tuers 2006;Oswald et al 2015), information systems (Gelbard et al 2018;Jung and Suh 2019) and machine learning (Lawanot et al 2019;LiKamWa et al 2013). However, buzz about worker well-being, enthusiasm for new programs to promote it and interest to research it have not been accompanied by universal enthusiasm for scientific measurement on the work floor.…”

mentioning

confidence: 99%

Worker Well-Being: What it Is, and how it Should Be Measured

Wijngaards

King

Burger

et al. 2021

Applied Research Quality Life

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Worker well-being is a hot topic in organizations, consultancy and academia. However, too often, the buzz about worker well-being, enthusiasm for new programs to promote it and interest to research it, have not been accompanied by universal enthusiasm for scientific measurement. Aim to bridge this gap, we address three questions. To address the question ‘What is worker well-being?’, we explain that worker well-being is a multi-facetted concept and that it can be operationalized in a variety of constructs. We propose a four-dimensional taxonomy of worker well-being constructs to illustrate the concept’s complexity and classify ten constructs within this taxonomy. To answer the question ‘How can worker well-being constructs be measured?’, we present two aspects of measures: measure obtrusiveness (i.e., the extent to which obtaining a measure interferes with workers’ experiences) and measure type (i.e., closed question survey, word, behavioral and physiological). We illustrate the diversity of measures across our taxonomy and uncover some hitherto under-appreciated avenues for measuring worker well-being. Finally, we address the question ‘How should a worker well-being measure be selected?’ by discussing conceptual, methodological, practical and ethical considerations when selecting a measure. We summarize these considerations in a short checklist. It is our hope that with this study researchers – working in organizations, in academia or both – will feel more competent to find effective strategies for the measurement worker well-being and eventually make policies and choices with a better understanding of what drives worker well-being.

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Daily Stress and Mood Recognition System Using Deep Learning and Fuzzy Clustering for Promoting Better Well-Being

Cited by 12 publications

References 17 publications

SaYoPillow: A Blockchain-Enabled, Privacy-Assured Framework for Stress Detection, Prediction and Control Considering Sleeping Habits in the IoMT

SaYoPillow: A Blockchain-Enabled, Privacy-Assured Framework for Stress Detection, Prediction and Control Considering Sleeping Habits in the IoMT

Prevención del síndrome de Burnout mediante programación neurolingüística soportada por la Web de las Cosas: Mapeo sistemático

Worker Well-Being: What it Is, and how it Should Be Measured

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