scholarly journals Wearable Technologies for Mental Workload, Stress, and Emotional State Assessment during Working-Like Tasks: A Comparison with Laboratory Technologies

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2332
Author(s):  
Andrea Giorgi ◽  
Vincenzo Ronca ◽  
Alessia Vozzi ◽  
Nicolina Sciaraffa ◽  
Antonello di Florio ◽  
...  
Keyword(s):  
Emotional State ◽  
Mental Workload ◽  
Electrodermal Activity ◽  
Wearable Sensors ◽  
Mental States ◽  
State Assessment ◽  
Ease Of Use ◽  
Laboratory Equipment ◽  
Human Errors ◽  
Work Related

The capability of monitoring user’s performance represents a crucial aspect to improve safety and efficiency of several human-related activities. Human errors are indeed among the major causes of work-related accidents. Assessing human factors (HFs) could prevent these accidents through specific neurophysiological signals’ evaluation but laboratory sensors require highly-specialized operators and imply a certain grade of invasiveness which could negatively interfere with the worker’s activity. On the contrary, consumer wearables are characterized by their ease of use and their comfortability, other than being cheaper compared to laboratory technologies. Therefore, wearable sensors could represent an ideal substitute for laboratory technologies for a real-time assessment of human performances in ecological settings. The present study aimed at assessing the reliability and capability of consumer wearable devices (i.e., Empatica E4 and Muse 2) in discriminating specific mental states compared to laboratory equipment. The electrooculographic (EOG), electrodermal activity (EDA) and photoplethysmographic (PPG) signals were acquired from a group of 17 volunteers who took part to the experimental protocol in which different working scenarios were simulated to induce different levels of mental workload, stress, and emotional state. The results demonstrated that the parameters computed by the consumer wearable and laboratory sensors were positively and significantly correlated and exhibited the same evidences in terms of mental states discrimination.

scholarly journals CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 52
Author(s):  
Tianyi Zhang ◽  
Abdallah El Ali ◽  
Chen Wang ◽  
Alan Hanjalic ◽  
Pablo Cesar
Keyword(s):  
Emotion Recognition ◽  
Electrodermal Activity ◽  
Short Form ◽  
Recognition Performance ◽  
Binary Classification ◽  
Wearable Sensors ◽  
Recognition Algorithm ◽  
Fine Grained ◽  
Physiological Sensors ◽  
Valence And Arousal

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

scholarly journals Diabetes classification application with efficient missing and outliers data handling algorithms

2021 ◽  
Author(s):  
Hanaa Torkey ◽  
Elhossiny Ibrahim ◽  
EZZ El-Din Hemdan ◽  
Ayman El-Sayed ◽  
Marwa A. Shouman
Keyword(s):  
Deep Learning ◽  
Healthcare System ◽  
Wearable Sensors ◽  
Model Performance ◽  
Analysis Model ◽  
Human Errors ◽  
Web Based ◽  
Machine Learning Model ◽  
Complete Dataset ◽  
Smart Wearable

AbstractCommunication between sensors spread everywhere in healthcare systems may cause some missing in the transferred features. Repairing the data problems of sensing devices by artificial intelligence technologies have facilitated the Medical Internet of Things (MIoT) and its emerging applications in Healthcare. MIoT has great potential to affect the patient's life. Data collected from smart wearable devices size dramatically increases with data collected from millions of patients who are suffering from diseases such as diabetes. However, sensors or human errors lead to missing some values of the data. The major challenge of this problem is how to predict this value to maintain the data analysis model performance within a good range. In this paper, a complete healthcare system for diabetics has been used, as well as two new algorithms are developed to handle the crucial problem of missed data from MIoT wearable sensors. The proposed work is based on the integration of Random Forest, mean, class' mean, interquartile range (IQR), and Deep Learning to produce a clean and complete dataset. Which can enhance any machine learning model performance. Moreover, the outliers repair technique is proposed based on dataset class detection, then repair it by Deep Learning (DL). The final model accuracy with the two steps of imputation and outliers repair is 97.41% and 99.71% Area Under Curve (AUC). The used healthcare system is a web-based diabetes classification application using flask to be used in hospitals and healthcare centers for the patient diagnosed with an effective fashion.

scholarly journals МENTAL STATES AND THE PROBLEM OF SELF-REGULATION OF ELDERLY PEOPLE

2020 ◽  
Vol 64 (3) ◽  
pp. 127-131
Author(s):  
N. Auyelbekova ◽  
◽  
N. Akhtaeva ◽  
Keyword(s):  
Elderly People ◽  
Emotional State ◽  
Self Regulation ◽  
Elderly Person ◽  
The Elderly ◽  
Mental States ◽  
Inner Experiences ◽  
Initial Results ◽  
Research Analysis

The article touches upon the problem of self-regulation of mental states of the elderly. The features of the emotional state of elderly people are described. Variants of effective self-regulation are proposed, the causes of negative states and ways to overcome them are analyzed. Purpose of the research: analysis of the emotional state of people over 60 and a way to successfully overcome the internal crisis through self-regulation. The article describes the initial results of a study of 20 respondents. The total number is planned in the amount of 80 people, the methods used in the study are listed. The article identifies the fulcrum, thanks to which an elderly person can cope with his inner experiences and find peace and harmony

scholarly journals Phraseological units as means of expressing the emotivity in the Tatar language

2021 ◽  
pp. 154-161
Author(s):  
Gulshat Raisovna Galiullina ◽  
Gulfiya Kamilovna Khadieva ◽  
Zilya Mullakhmetovna Mukhametgalieva ◽  
Margarita Emilievna Dubrovina
Keyword(s):  
Emotional State ◽  
Life Experience ◽  
Mental States ◽  
Scientific Field ◽  
Wilhelm Von Humboldt ◽  
Emotional Environment ◽  
Field Linguistics ◽  
Inner World ◽  
The Given ◽  
Negative Connotation

Systematization and description of the arsenal of linguistic means of expressing emotions represent one of the major tasks for linguistics that returns nowadays to the theory of Wilhelm von Humboldt, which in the early XIXth century appealed to study the language in close connection with individual speakers. A logical interest of the researchers to the processes of manifestation of emotions in the language has resulted in the formation of a new scientific field – linguistics of emotions aimed at the emotional environment of the language. In the Tatar language human emotions are verbalized mostly by the phraseological units representing various mental states of a person, one’s inner world. Studying means of expressing emotive vocabulary illustrated by the phraseological units provides an opportunity to present the whole complex of means of the language and the speech, as well as contribute to understanding the mentality and psychology of a Tatar language person. This article covers the Tatar phraseological units expressing negative connotation. The theme group “anger” represents the object of research. The authors have studied the emotional and appraisal semantics of the given group of phraseological units and attempted the revealing the specificity of the way of thinking and the worldview of the Tatar people. The analysis revealed that the phraseological units of the studied group are characterized by a great diversity of lexical, semantic, emotional and appraisal aspects. The emotional and appraisal volume of the phraseological units varies depending on the emotional state of the speaker and on his attitude to the addressee. Cultural and connotative semantics of the phraseological units is closely connected to the Tatar people’s worldview which has formed and has been enriched throughout the life experience.

Exploring Personal Healthcare with the Help of Two Large European Framework Programs for Healthcare

2010 ◽  
pp. 918-938
Author(s):  
Harald Reiter ◽  
Joerg Habetha
Keyword(s):  
Chronic Diseases ◽  
New Technologies ◽  
Wearable Sensors ◽  
Ease Of Use ◽  
The Self ◽  
Self Management ◽  
European Research ◽  
Research Projects ◽  
Personal Healthcare

Personal healthcare enables prevention and early diagnosis in daily life and is centered on the patient. There is a need for a new personal healthcare paradigm in the treatment of chronic diseases. This will be achieved by new technologies that are currently explored (e.g., in European Research projects such as MyHeart and HeartCycle). These projects develop technologies and application concepts for the (self-)management of chronic diseases in patients’ homes with special emphasis on usability and ease-of-use (e.g., wearable sensors and processing units that can even be integrated into the patient’s clothes). These technologies allow empowering patients, fostering self-management and therefore reducing cost, and improving patients’ quality of life.

Musculoskeletal disorders within the carpentry trade: analysis of timber flooring subtasks

2020 ◽  
Vol 27 (9) ◽  
pp. 2577-2590
Author(s):  
Abiola Akanmu ◽  
Johnson Olayiwola ◽  
Oluwole Alfred Olatunji
Keyword(s):  
Musculoskeletal Disorders ◽  
Body Movement ◽  
Wearable Sensors ◽  
Body Parts ◽  
Content Type ◽  
Work Related ◽  
Movement Data ◽  
Cutting Out ◽  
Trade Analysis ◽  
Ergonomic Exposures

PurposeCarpenters are constantly vulnerable to musculoskeletal disorders. Their work consists of subtasks that promote nonfatal injuries and pains that affect different body segments. The purpose of this study is to examine ergonomic exposures of carpentry subtasks involved in floor framing, how they lead to musculoskeletal injuries, and how preventive and protective interventions around them can be effective.Design/methodology/approachUsing wearable sensors, this study characterizes ergonomic exposures of carpenters by measuring and analyzing body movement data relating to major subtasks in carpentry flooring work. The exposures are assessed using Postural Ergonomic Risk Assessment classification, which is based on tasks involving repetitive subtasks and nonstatic postures.FindingsThe findings of this paper suggest severe risk impositions on the trunk, shoulder and elbow as a result of the measuring and marking and cutting out vent locations, as well as in placing and nailing boards into place.Research limitations/implicationsBecause of the type and size of wearable sensor used, only results of risk exposures of four body-parts are presented.Practical implicationsThis study draws insights on how to benchmark trade-specific measurement of work-related musculoskeletal disorders. Safety efforts can be targeted toward these risk areas and subtasks. Specifically, results from these will assist designers and innovators in designing effective and adaptable protective interventions and safety trainings.Originality/valueExtant studies have failed to provide adequate evidence regarding the relationships between subtasks and musculoskeletal disorders; they have only mimicked construction tasks through laboratory experimental scenarios. This study adds value to the existing literature, in particular by providing insights into hazards associated with floor carpentry subtasks.

scholarly journals Effects of a short residential thermal spa program to prevent work-related stress/burnout on stress biomarkers: the ThermStress proof of concept study

2019 ◽  
Vol 47 (10) ◽  
pp. 5130-5145
Author(s):  
Frédéric Dutheil ◽  
Elodie Chaplais ◽  
Audrey Vilmant ◽  
Denise Lanoir ◽  
Daniel Courteix ◽  
...  
Keyword(s):  
Psychological Intervention ◽  
Electrodermal Activity ◽  
Public Health Issue ◽  
Proof Of Concept ◽  
Psychological Consequences ◽  
Work Related Stress ◽  
Ongoing Research ◽  
Concept Study ◽  
Work Related ◽  
Related Stress

Objective Work-related stress is a public health issue. Stress has multiple physical and psychological consequences, the most serious of which are increased mortality and cardiovascular morbidity. The ThermStress protocol was designed to offer a short residential thermal spa program for work-related stress prevention that is compatible with a professional context. Methods Participants will be 56 male and female workers aged 18 years or above. All participants will undergo a 6-day residential spa program comprising psychological intervention, physical activity, thermal spa treatment, health education, eating disorder therapy and a follow-up. On six occasions, participants’ heart rate variability, cardiac remodelling and function, electrodermal activity, blood markers, anthropometry and body composition, psychology and quality of life will be measured using questionnaires and bone parameters. Results This study protocol reports the planned and ongoing research for this intervention. Discussion The ThermStress protocol has been approved by an institutional ethics committee (ANSM: 2016 A02082 49). It is expected that this proof of concept study will highlight the effect of a short-term specific residential thermal spa program on the prevention of occupational burnout and work-related stress. The findings will be disseminated at several research conferences and in published articles in peer-reviewed journals. Trial Registration: ClinicalTrials.gov (NCT 03536624, 24/05/2018)

scholarly journals P-Ergonomics Platform: Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1225 ◽  
Author(s):  
Mario Vega-Barbas ◽  
Jose Diaz-Olivares ◽  
Ke Lu ◽  
Mikael Forsman ◽  
Fernando Seoane ◽  
...  
Keyword(s):  
Wearable Sensors ◽  
Behavioral Changes ◽  
Risk Assessments ◽  
Cloud Platform ◽  
Preventive Healthcare ◽  
Work Related ◽  
System Usability Scale ◽  
Amazon Web Services ◽  
Continuous Feedback ◽  
Medium Enterprises

Preventive healthcare has attracted much attention recently. Improving people’s lifestyles and promoting a healthy diet and wellbeing are important, but the importance of work-related diseases should not be undermined. Musculoskeletal disorders (MSDs) are among the most common work-related health problems. Ergonomists already assess MSD risk factors and suggest changes in workplaces. However, existing methods are mainly based on visual observations, which have a relatively low reliability and cover only part of the workday. These suggestions concern the overall workplace and the organization of work, but rarely includes individuals’ work techniques. In this work, we propose a precise and pervasive ergonomic platform for continuous risk assessment. The system collects data from wearable sensors, which are synchronized and processed by a mobile computing layer, from which exposure statistics and risk assessments may be drawn, and finally, are stored at the server layer for further analyses at both individual and group levels. The platform also enables continuous feedback to the worker to support behavioral changes. The deployed cloud platform in Amazon Web Services instances showed sufficient system flexibility to affordably fulfill requirements of small to medium enterprises, while it is expandable for larger corporations. The system usability scale of 76.6 indicates an acceptable grade of usability.

scholarly journals Evaluating physiological signal salience for estimating metabolic energy cost from wearable sensors

2019 ◽  
Vol 126 (3) ◽  
pp. 717-729 ◽  
Author(s):  
Kimberly A. Ingraham ◽  
Daniel P. Ferris ◽  
C. David Remy
Keyword(s):  
Heart Rate ◽  
Steady State ◽  
Indirect Calorimetry ◽  
Energy Cost ◽  
Electrodermal Activity ◽  
Wearable Sensors ◽  
Metabolic Energy ◽  
Physiological Signals ◽  
Physiological Signal ◽  
Signal Salience

Body-in-the-loop optimization algorithms have the capability to automatically tune the parameters of robotic prostheses and exoskeletons to minimize the metabolic energy expenditure of the user. However, current body-in-the-loop algorithms rely on indirect calorimetry to obtain measurements of energy cost, which are noisy, sparsely sampled, time-delayed, and require wearing a respiratory mask. To improve these algorithms, the goal of this work is to predict a user’s steady-state energy cost quickly and accurately using physiological signals obtained from portable, wearable sensors. In this paper, we quantified physiological signal salience to discover which signals, or groups of signals, have the best predictive capability when estimating metabolic energy cost. We collected data from 10 healthy individuals performing 6 activities (walking, incline walking, backward walking, running, cycling, and stair climbing) at various speeds or intensities. Subjects wore a suite of physiological sensors that measured breath frequency and volume, limb accelerations, lower limb EMG, heart rate, electrodermal activity, skin temperature, and oxygen saturation; indirect calorimetry was used to establish the ‘ground truth’ energy cost for each activity. Evaluating Pearson’s correlation coefficients and single and multiple linear regression models with cross validation (leave-one- subject-out and leave-one- task-out), we found that 1) filtering the accelerations and EMG signals improved their predictive power, 2) global signals (e.g., heart rate, electrodermal activity) were more sensitive to unknown subjects than tasks, while local signals (e.g., accelerations) were more sensitive to unknown tasks than subjects, and 3) good predictive performance was obtained combining a small number of signals (4–5) from multiple sensor modalities. NEW & NOTEWORTHY In this paper, we systematically compare a large set of physiological signals collected from portable sensors and determine which sensor signals contain the most salient information for predicting steady-state metabolic energy cost, robust to unknown subjects or tasks. This information, together with the comprehensive data set that is published in conjunction with this paper, will enable researchers and clinicians across many fields to develop novel algorithms to predict energy cost from wearable sensors.

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