A study on the stress identification using observed heart beat data

Pramanta, S.P.L. Aditya; Prihatmanto, Ary Setijadi; Park, Man-Gon

doi:10.1109/fit.2016.7857555

Cited by 3 publications

(4 citation statements)

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“…The results of that study suggest that work stress can cause increased heart-rate reactivity to a stressful workday, an increase in systolic BP, and lower vagal tone. In another study, Hudson [4] used a machine-learning approach to predict increases in BP. As today's world is moving towards intelligent systems, the demand for collaboration between various fields is increasing.…”

Section: Literature Review and The State Of The Artmentioning

confidence: 99%

“…Some of the studies are based on stress questionnaires, which are commonly used by psychologists to detect patients' stress levels; for example, a research conducted by Sheldon Cohen used a questionnaire for the reliability and validity of a 14-item instrument, the Perceived Stress Scale (PSS), which is designed to measure the degree to which situations in one's life are appraised as stressful [1]. Heart rate is also used as a parameter in various studies on stress identification [4].…”

Section: Introductionmentioning

confidence: 99%

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A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London

Tamang¹,

Sharif²,

Al-Bayatti

et al. 2020

Symmetry

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The daily commute represents a source of chronic stress that is positively correlated with physiological consequences, including increased blood pressure, heart rate, fatigue, and other negative mental and physical health effects. The purpose of this research is to investigate and predict the physiological effects of commuting in Greater London on the human body based on machine-learning approaches. For each participant, the data were collected for five consecutive working days, before and after the commute, using non-invasive wearable biosensor technology. Multimodal behaviour, analysis and synthesis are the subjects of major efforts in computing field to realise the successful human–human and human–agent interactions, especially for developing future intuitive technologies. Current analysis approaches still focus on individuals, while we are considering methodologies addressing groups as a whole. This research paper employs a pool of machine-learning approaches to predict and analyse the effect of commuting objectively. Comprehensive experimentation has been carried out to choose the best algorithmic structure that suit the problem in question. The results from this study suggest that whether the commuting period was short or long, all objective bio-signals (heat rate and blood pressure) were higher post-commute than pre-commute. In addition, the results match both the subjective evaluation obtained from the Positive and Negative Affect Schedule and the proposed objective evaluation of this study in relation to the correlation between the effect of commuting on bio-signals. Our findings provide further support for shorter commutes and using the healthier or active modes of transportation.

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Section: Literature Review and The State Of The Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London

Tamang¹,

Sharif²,

Al-Bayatti

et al. 2020

Symmetry

View full text Add to dashboard Cite

show abstract

“…U-workers in the place such as car, home, street or hot spot area can collect, store, manage, and analyze data through PC, PDA, or iPhone among various u-work services which are the major components of the platform. It serves as the hub to integrate techniques and functions that are associated with u-work services and provides an environment to develop and run the u-work services [20]. Besides, the platform provides service process templates, steps of a process, and work data structures, which allow service developers to easily create new u-work services based on the process template, and by considering specific concerns of the target user group and the environment.…”

Section: A the Concepts Of Computer-supported Cooperative Work And Umentioning

confidence: 99%

“…This situation suggests that the design of teaching and learning needs to consider added dimensions: flexible content display, and the possibilities of delivering teaching and learning in random contexts [29], but also potentially right at the exact context. It is difficult to find an accurate distinction between m-learning and u-learning [20] because it has a big pedagogical common area. However, we can classify u-learning as the intersection area of elearning and m-learning as integrated space, as shown in Fig.…”

Section: A Shifts From E-learning and M-learning To U-learningmentioning

confidence: 99%

Ubiquitous computing: a learning system solution in the era of industry 4.0

Toyib¹,

Agusman²,

Ramza³

2020

Jurnal Informatika

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State-of-the-Art of Stress Prediction from Heart Rate Variability Using Artificial Intelligence

Haque,

Zawad,

Rony

et al. 2023

Cogn Comput

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Recent advancements in the manufacturing and commercialisation of miniaturised sensors and low-cost wearables have enabled an effortless monitoring of lifestyle by detecting and analysing physiological signals. Heart rate variability (HRV) denotes the time interval between consecutive heartbeats.The HRV signal, as detected by the sensors and devices, has been popularly used as an indicative measure to estimate the level of stress, depression, and anxiety. For years, artificial intelligence (AI)-based learning systems have been known for their predictive capabilities, and in recent years, AI models with deep learning (DL) architectures have been successfully applied to achieve unprecedented accuracy. In order to determine effective methodologies applied to the collection, processing, and prediction of stress from HRV data, this work presents an in depth analysis of 43 studies reporting the application of various AI algorithms. The methods are summarised in tables and thoroughly evaluated to ensure the completeness of their findings and reported results. To make the work comprehensive, a detailed review has been conducted on sensing technologies, pre-processing methods applied on multi-modal data, and employed prediction models. This is followed by a critical examination of how various Machine Learning (ML) models, have been utilised in predicting stress from HRV data. In addition, the reported reseults from the selected studies have been carefully analysed to identify features that enable the models to perform better. Finally, the challenges of using HRV to predict stress are listed, along with some possible mitigation strategies. This work aims to highlight the impact of AI-based stress prediction methodologies from HRV data, and is expected to aid the development of more meticulous techniques.

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A study on the stress identification using observed heart beat data

Cited by 3 publications

References 0 publications

A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London

A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London

Ubiquitous computing: a learning system solution in the era of industry 4.0

State-of-the-Art of Stress Prediction from Heart Rate Variability Using Artificial Intelligence

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