Stress Diagnostic System and Digital Medical Record Based on Internet of Things

Setiawan, Rachmad; Budiman, Fajar; Basori, Wahyu Irfan

doi:10.1109/isitia.2019.8937273

Cited by 14 publications

(4 citation statements)

References 4 publications

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“…Disadvantages Applications Fuzzy Logic Algorithm [22], [25] No precise inputs are required, occupies a small memory space as the code is small, able to solve complex problems, constructs easily due to simple structure, helps to copy the logic of human thoughts, highly useful for uncertain or approximate reasoning.…”

Section: Advantagesmentioning

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: Advantagesmentioning

confidence: 99%

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

2021

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“…• Random Forest [393]- [396] • Support Vector Machine (SVM) [397]- [401] • k-Nearest Neighbors (kNN) [402]- [404] • Fuzzy Logic [405]- [408] • Decision Trees [409], [410] • Principal Component Analysis (PCA) [411]- [413] • Artificial Neural Network (ANN) [414]- [416] • Ensemble Methods [417], [418] • Naïve Bayes [419], [420] • Linear Discriminant Analysis [421]- [423]…”

Section: A Artificial Intelligence (Ai)mentioning

confidence: 99%

Review of Stress Detection Methods Using Wearable Sensors

Taskasaplidis,

Fotiadis,

Bamidis

2024

IEEE Access

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Stress is a significant factor that affects well-being and health. Factors that trigger stress include work, social interactions, and economic and environmental factors. Stress may cause lower labor productivity, physical and mental health problems, and malfunctions in all social aspects of life. Psychosomatic health can be improved if proper stress detection mechanisms are present in daily life and stress reduction methods can occur. Wearable sensors are currently used in many commercial and scientific applications in a non-destructive or annoying manner. These devices are used in daily routines. In this paper, a comprehensive review of the latest literature and developments in stress detection methods is presented through extensive and holistic research on stress response, both at the level of the autonomic nervous system (ANS) and hypothalamic-pituitary-adrenal axis (HPA). This study focused on the exploitation of various methods, technologies, and data analysis systems to understand stress in a multifaceted and comprehensive manner. Various stress-related factors are presented along with biological signal measurements, and physical secretions or biomarkers are primarily used for stress detection. Furthermore, the manner in which body movement and posture measurements may be related to stress was investigated, together with speech and hand tremors. Various stress-detection technologies have been analyzed, and existing data analysis methods that can be applied to stress-detection systems have been highlighted. This review serves as a reference and guideline for exploring this area of interest, identifying research opportunities, and offering ideas, options, and suggestions for optimized solutions regarding future applications and research.

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“…Though digital systems that help control healthcare infrastructure have been used for a couple of decades, many advancements, including the IoT, can further benefit mankind tremendously [ 66 ]. In an IoT-enabled infrastructure, the following tasks are given importance: management of data of patients [ 67 ], emergency management [ 68 , 69 ], management of inventory [ 70 , 71 , 72 ], resource scheduling [ 73 ], error prevention [ 74 , 75 ], etc.…”

Section: Fundamentals Of Internet Of Things-enabled Healthcare Systemsmentioning

confidence: 99%

A Survey of Authentication in Internet of Things-Enabled Healthcare Systems

Khan

Din

Majali

et al. 2022

Sensors

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The Internet of medical things (IoMT) provides an ecosystem in which to connect humans, devices, sensors, and systems and improve healthcare services through modern technologies. The IoMT has been around for quite some time, and many architectures/systems have been proposed to exploit its true potential. Healthcare through the Internet of things (IoT) is envisioned to be efficient, accessible, and secure in all possible ways. Even though the personalized health service through IoT is not limited to time or location, many associated challenges have emerged at an exponential pace. With the rapid shift toward IoT-enabled healthcare systems, there is an extensive need to examine possible threats and propose countermeasures. Authentication is one of the key processes in a system’s security, where an individual, device, or another system is validated for its identity. This survey explores authentication techniques proposed for IoT-enabled healthcare systems. The exploration of the literature is categorized with respect to the technology deployment region, as in cloud, fog, and edge. A taxonomy of attacks, comprehensive analysis, and comparison of existing authentication techniques opens up possible future directions and paves the road ahead.

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Stress Diagnostic System and Digital Medical Record Based on Internet of Things

Cited by 14 publications

References 4 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

Review of Stress Detection Methods Using Wearable Sensors

A Survey of Authentication in Internet of Things-Enabled Healthcare Systems

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