Analysing IoT Data for Anxiety and Stress Monitoring: A Systematic Mapping Study and Taxonomy

International Journal of Human-Computer Interaction, Год журнала: 2022, Номер 40(5), С. 1174 - 1194

Опубликована: Окт. 27, 2022

Anxiety and stress are common emotional responses for human beings, but their chronic manifestation can lead to physical psychological illnesses. The advancement of sensing technologies, such as Internet Things, has contributed the understanding assisting events related anxiety stress. However, main challenge is knowing which approaches be used better monitor these emotions assist people. Based on a systematic literature review, this work analyzed studies both determine how data collected, levels. Two taxonomies synthesize techniques mapped. results indicated more emphasis studying than focus detecting levels user. Among collect data, 62.5% physiological like heart analysis techniques, 48% Decision Trees.

Язык: Английский

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Face Emotion Recognition System for Depression Detection Using AI Techniques

International Journal for Research in Applied Science and Engineering Technology, Год журнала: 2024, Номер 12(1), С. 175 - 182

Опубликована: Янв. 8, 2024

Abstract: The human face is an essential aspect of individual's body. It plays a crucial function in detecting and identifying emotions since the where person exhibits all their fundamental emotions. Through emotions, we solve different types problems. Like healthcare, security, business, education. purpose this paper to present detection depression mental health sector. Depression or stress faced by most population over world for many reasons at stages life. As current life busy cycle, gets depressed stressed daily may be found educational activities, competitive challenging tasks, employment pressure, family consequences, sorts connection management, issues, old age, other situations. Artificial intelligence deep learning approaches are suggested study assess depression. This research useful analyzing every employer psychologist when counselling patients. Here, propose convolutional neural network (DCNN) model. model can classify two facial Which based on positive negative trained tested using FER-2013 dataset. data set used experimentation FER (Facial Expression Recognition) dataset available KAGGLE repository. implementation environment includes Keras, TensorFlow, OpenCV Python packages. result emotion accuracy between training test phases. average achieved was 77%.

Язык: Английский

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Stress Detection:Detecting, Monitoring, and Reducing Stress in Cyber-Security Operation Centers Using Facial Expression Recognition Software

Опубликована: Янв. 1, 2024

The accessibility of technology and the IOT (Internet Things) available at our fingertips, twenty-four hours per day, can quickly become a technological overload. For many reasons, this be problematic for people working in front computers, whether home or an office setting. Our "first responders" cybersecurity, analyst that works consistently multiple computer screens mobile devices to protect data from various forms attacks are higher risk. Unmanaged stress lead poor job performance such as health-related absences, missed warning signals on possible cyberattacks, overall lack enthusiasm work needed done.

Язык: Английский

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Can heart rate sequences from wearable devices predict day-long mental states in higher education students: a signal processing and machine learning case study at a UK university

Brain Informatics, Год журнала: 2024, Номер 11(1)

Опубликована: Дек. 1, 2024

Abstract The mental health of students in higher education has been a growing concern, with increasing evidence pointing to heightened risks developing condition. This research aims explore whether day-long heart rate sequences, collected continuously through Apple Watch an open environment without restrictions on daily routines, can effectively indicate states, particularly stress for university students. While (HR) is commonly used monitor physical activity or responses isolated stimuli controlled setting, such as stress-inducing tests, this study addresses the gap by analyzing fluctuations throughout day, examining their potential gauge overall levels more comprehensive and real-world context. data was at public UK. Using signal processing, both original sequences representations, via Fourier transformation wavelet analysis, have modeled using advanced machine learning algorithms. Having achieving statistically significant results over baseline, provides understanding how alone may be characterize states processing learning, system poised further testing ongoing collection continues.

Язык: Английский

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Detection and monitoring of stress using wearables: a systematic review

Frontiers in Computer Science, Год журнала: 2024, Номер 6

Опубликована: Дек. 18, 2024

Over the last few years, wearable devices have witnessed immense changes in terms of sensing capabilities. Wearable devices, with their ever-increasing number sensors, been instrumental monitoring human activities, health-related indicators, and overall wellness. One area that has rapidly adopted is mental health well-being area, which covers problems such as psychological distress. The continuous capability allows detection stress, thus enabling early problems. In this paper, we present a systematic review different types sensors used by researchers to detect monitor stress individuals. We identify detail tasks data collection, pre-processing, features computation, training model explored research works. each step involved monitoring. also discuss scope opportunities for further deals management once it detected.

Язык: Английский

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