A Comprehensive Review on Stress Detection Techniques

Shanmugasundaram, G.; Yazhini, S.; Hemapratha, E.; Nithya, S.

doi:10.1109/icscan.2019.8878795

Cited by 30 publications

(12 citation statements)

References 10 publications

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“…There are some surveys that lack exhaustive review [9]- [11] with regards to the topic of stress detection, whereas, surveys [7], [12]- [14] covers comprehensive information. However, no collective information on machine learning and wearable sensors was described in any of these reviews.…”

Section: Discussionmentioning

confidence: 99%

“…• Classification accuracies obtained with specific models have been mentioned (in Tables no. [5][6][7][8][9][10][11] This can provide a clear guideline for fellow researchers in this field. • Very few standards and publicly available datasets were used and most researchers built their own realtime datasets.…”

Section: Discussionmentioning

confidence: 99%

“…For real-time monitoring of stress, various wearable commercial devices are available in the market. Some researchers have used these devices for data collection and continuous monitoring of stress levels of users while some have developed their own wearable devices [8], [9], [11], [12], [14] made from low-cost sensors for ambulatory monitoring of stress. These devices must measure the signals with minimal error and noise for achieving better accuracy of the algorithm.…”

Section: Real-time Stress Monitoringmentioning

confidence: 99%

“…There are some surveys available in this area of automatic stress detection. In [9], the authors studied some physiological parameters and concluded that the best among them was the one that was influenced by pressure and proposed an appropriate framework to use in stress recognition. They also summarized techniques to analyze and recognize stress by using various wearable sensors.…”

Section: Existing Surveys and Reviews On Stress Detectionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Real-time Stress Monitoringmentioning

confidence: 99%

Section: Existing Surveys and Reviews On Stress Detectionmentioning

confidence: 99%

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

2021

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“…Although stress is not a disease, it is the first sign of a larger problem that can result in long-term damages [8]. Among the long-term consequences of work stress are; reduced productivity, lower quality of life, heart diseases, asthma, headaches, depression, anxiety [9], risk of addictions [6], musculoskeletal discomfort (MSD), exhaustion at work [4], and the onset of Burnout Syndrome (BS) [10], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Working Hours, Burnout and Musculoskeletal Discomfort in Middle and Senior Management of Mexican Industrial Sector

Macias-Velasquez¹,

Báez-López²,

Maldonado-Macías

et al. 2020

IEEE Access

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Work-related stress is one of the ten most frequent occupational health problems worldwide and precedes a variety of serious mental disorders. It occurs when employees' capabilities fail to meet job demands or when their knowledge and skills are insufficient to comply with company expectations. The aim of this research was to evaluate the impact of working hours and burnout syndrome (BS) on the appearance of musculoskeletal discomfort (MSD) through three dimensions. A cross-sectional study was conducted on 472 middle and senior management staff in the industrial sector in Mexico. Structural Equation Modeling was used to evaluate the relationship between variables. The results showed that 54% of participants reported to be working a maximum of 48 hours a week, the four most frequent MSD occurring in the low back, upper back, neck and eyes. Regarding BS, 59.6% of the participants experienced it at levels ranging from medium to extreme. These findings showed that working hours have a direct impact on emotional exhaustion and indirectly affect MSD and the dimensions of professional efficacy and cynicism. In addition, discomfort in the upper back is directly associated to ailments in the low back and neck and indirectly, to discomfort in the eyes. This points to the need for a greater focus on developing specific measures to reduce or prevent musculoskeletal discomfort symptoms which result from employees' long working hours and emotional exhaustion.INDEX TERMS Burnout syndrome, musculoskeletal discomfort, structural equation modeling, working hours.

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A Systematic Review of Thermal and Cognitive Stress Indicators: Implications for Use Scenarios on Sensor-Based Stress Detection

Carrizosa-Botero

Rendón-Vélez

2021

Human-Computer Interaction – INTERACT 2021

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A Comprehensive Review on Stress Detection Techniques

Cited by 30 publications

References 10 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

Working Hours, Burnout and Musculoskeletal Discomfort in Middle and Senior Management of Mexican Industrial Sector

A Systematic Review of Thermal and Cognitive Stress Indicators: Implications for Use Scenarios on Sensor-Based Stress Detection

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