Correlation Analyses Between Personality Traits and Personal Behaviors Under Specific Emotion States Using Physiological Data from Wearable Devices

The aim of the present study was to analyse the personality traits and value preferences of students from integrated and non-integrated classes. Sixty-nine primary school sixth graders were surveyed (M = 12.45; SD = .58). The group of students attending integrated classes included 38 individuals. The remaining 31 students attended non-integrated classrooms. Personality traits were measured using the Picture-Based Personality Survey for Children (PBPS-C ) and value preferences were determined on the basis of the Picture-Based Value Survey for Children (PBVS-C). The results showed that youth from the integrated classes did not differ significantly from their peers from the non-integrated classes in terms of personality traits. In case of values, students from the non-integrated classes cherished values of Universalism more than their peers from the integrated classes. Correlation analyses showed that the patterns of relations between personality traits and preferred values were partially different for the two groups. Nevertheless, a similar pattern of relations was observed in both groups between Openness to Experience and values in the categories of Self-direction and Universalism.

Download Full-text

A demand for wearable devices in health care

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.7.9377 ◽

2018 ◽

Vol 7 (1.7) ◽

pp. 1 ◽

Cited By ~ 6

Author(s):

K. Vijayalakshmi ◽

S. Uma ◽

R. Bhuvanya ◽

A. Suresh

Keyword(s):

Digital Health ◽

Wearable Devices ◽

Physiological Data ◽

Wireless Data ◽

Telecommunication System ◽

Future Directions ◽

Research Survey ◽

Wireless Data Transmission ◽

Body Activity

With the popularity of wearable devices, along with the development of telecommunication system there is a need for obtaining the health and fitness outcomes. So the recent advances in data analysis techniques have opened up new possibilities for using wearable technology in the digital health ecosystem. In past, it’s too difficult to use the wearable devices for healthcare system because of the size of those sensors. But now with front end amplification and wireless data transmission, the wearable devices are deployed in health monitoring systems. Although the devices are continuously monitoring the human’s body activity and collect various physiological data to increase the quality of human’s life. In this paper first we provide a research survey on available wearable or gadgets. Also we conclude with future directions in wearable research and market.

Download Full-text

Beyond the Debate on Promises and Risks in Digital Health: Analysing the Psychological Function of Wearable Devices

International Journal of Psychological Studies ◽

10.5539/ijps.v8n4p26 ◽

2016 ◽

Vol 8 (4) ◽

pp. 26 ◽

Cited By ~ 2

Author(s):

Maria del Rio Carral ◽

Pauline Roux ◽

Christine Bruchez ◽

Marie Santiago-Delefosse

Keyword(s):

Lived Experience ◽

Digital Health ◽

Rapid Development ◽

Wearable Devices ◽

The Body ◽

Physiological Data ◽

Psychological Function ◽

Biometric Data ◽

Health Related ◽

Definition Of

<p>In the past years, the recording and collection of physical and physiological data from the body through wearable devices has become an increasingly common health-related practice in contemporary Western societies. The rapid development of digital self-tracking technologies has given rise to the production of different scientific discourses. The analysis of 200 published articles has led to the definition of a continuum between “technophile-promises” and “technocritical-risks” representations. However, these representations include different views of corporeality and sociality. Beyond this debate, we propose an alternative theoretical framework that links corporeality and sociality. It interrogates the psychological function that wearable devices may take (or not) for subjects to which these “tools” are addressed. We argue that such psychological function must be embraced by taking into consideration of activity done by the users of these technologies, which engages meaning: It is not the device, but the user him/herself who is confronted to the interpretation of biometric data linked to his/her own body functions on the basis of concrete lived experience. Moreover, we discuss that the activity of users can only be analysed in the sociocultural context to which the associated practices relate (health, sports, play, medicalisation). The conclusion highlights the need to further study the appropriation process of new personal experimentation instruments as to better understand the potential collaborations, risks or resistances that users may develop.</p>

Download Full-text

Intelligent Fog-Enabled Smart Healthcare System for Wearable Physiological Parameter Detection

Electronics ◽

10.3390/electronics9122015 ◽

2020 ◽

Vol 9 (12) ◽

pp. 2015

Author(s):

Muhammad Ijaz ◽

Gang Li ◽

Huiquan Wang ◽

Ahmed M. El-Sherbeeny ◽

Yussif Moro Awelisah ◽

...

Keyword(s):

Health Status ◽

Learning Algorithm ◽

Fog Computing ◽

Wearable Devices ◽

System Stability ◽

Kernel Principal Component Analysis ◽

Physiological Data ◽

Physiological Parameter ◽

Detection Accuracy ◽

Smart Healthcare

Wearable technology plays a key role in smart healthcare applications. Detection and analysis of the physiological data from wearable devices is an essential process in smart healthcare. Physiological data analysis is performed in fog computing to abridge the excess latency introduced by cloud computing. However, the latency for the emergency health status and overloading in fog environment becomes key challenges for smart healthcare. This paper resolves these problems by presenting a novel tri-fog health architecture for physiological parameter detection. The overall system is built upon three layers as wearable layer, intelligent fog layer, and cloud layer. In the first layer, data from the wearable of patients are subjected to fault detection at personal data assistant (PDA). To eliminate fault data, we present the rapid kernel principal component analysis (RK-PCA) algorithm. Then, the faultless data is validated, whether it is duplicate or not, by the data on-looker node in the second layer. To remove data redundancy, we propose a new fuzzy assisted objective optimization by ratio analysis (FaMOORA) algorithm. To timely predict the user’s health status, we enable the two-level health hidden Markov model (2L-2HMM) that finds the user’s health status from temporal variations in data collected from wearable devices. Finally, the user’s health status is detected in the fog layer with the assist of a hybrid machine learning algorithm, namely SpikQ-Net, based on the three major categories of attributes such as behavioral, biomedical, and environment. Upon the user’s health status, the immediate action is taken by both cloud and fog layers. To ensure lower response time and timely service, we also present an optimal health off procedure with the aid of the multi-objective spotted hyena optimization (MoSHO) algorithm. The health off method allows offloading between overloaded and underloaded fog nodes. The proposed tri-fog health model is validated by a thorough simulation performed in the iFogSim tool. It shows better achievements in latency (reduced up to 3 ms), execution time (reduced up to 1.7 ms), detection accuracy (improved up to 97%), and system stability (improved up to 96%).

Download Full-text

A Public Dataset of 24-h Multi-Levels Psycho-Physiological Responses in Young Healthy Adults

Data ◽

10.3390/data5040091 ◽

2020 ◽

Vol 5 (4) ◽

pp. 91 ◽

Cited By ~ 1

Author(s):

Alessio Rossi ◽

Eleonora Da Pozzo ◽

Dario Menicagli ◽

Chiara Tremolanti ◽

Corrado Priami ◽

...

Keyword(s):

Physiological Responses ◽

Wearable Devices ◽

Stressful Events ◽

Psychological Characteristics ◽

Physiological Data ◽

Heart Rate Data ◽

Life Data ◽

Public Dataset ◽

Open Datasets ◽

And Behavior

Wearable devices now make it possible to record large quantities of physiological data, which can be used to obtain a clearer view of a person’s health status and behavior. However, to the best of our knowledge, there are no open datasets in the literature that provide psycho-physiological data. The Multilevel Monitoring of Activity and Sleep in Healthy people (MMASH) dataset presented in this paper provides 24 h of continuous psycho-physiological data, that is, inter-beat intervals data, heart rate data, wrist accelerometry data, sleep quality index, physical activity (i.e., number of steps per second), psychological characteristics (e.g., anxiety status, stressful events, and emotion declaration), and sleep hormone levels for 22 participants. The MMASH dataset will enable the investigation of possible relationships between the physical and psychological characteristics of people in daily life. Data were validated through different analyses that showed their compatibility with the literature.

Download Full-text

Neurociencia y eneagrama: Reconfigurando los equipos de trabajo

Revista de Pedagogía Critica ◽

10.35429/jcp.2019.8.3.6.16 ◽

2019 ◽

pp. 6-16

Author(s):

Gildardo Linarez-Placencia ◽

Luz María Espinoza-Castelo

Keyword(s):

Experimental Study ◽

Personality Traits ◽

Quantitative Study ◽

Work Teams ◽

Physiological Data ◽

Knowledge Gap ◽

Positive Perception ◽

Team Members ◽

Quasi Experimental ◽

The Way

Competitiveness is outside modernity in all sectors of society. The execution of tasks has become too complex due to the problems brought by the integration of globalization as a synonym for competition. Undoubtedly, the way to respond to the complexity of the current environment is through the integration of work teams that can be efficient; And the only way to guarantee success is by supplementing the few personality traits developed with the well-worked skills of other team members. Therefore, this research developed a quasi-experimental study in 62 people; to demonstrate that work teams formed by eneatypes or personality traits, obtained by the enneagram map test, are more efficient than traditional equipment. The main contributions to the knowledge gap of this research are: demystification of the enneagram; demonstrating through a quantitative study that the participants obtain better results when working in teams formed with enneagram; participants have a positive perception about enneagram; and using the tools of neuroscience it is proved with physiological data that the theoretical precepts of enneagram are correct.

Download Full-text

Feasibility of continuous fever monitoring using wearable devices

10.21203/rs.3.rs-43914/v1 ◽

2020 ◽

Author(s):

Benjamin Smarr ◽

Kirstin Aschbacher ◽

Sarah M. Fisher ◽

Anoushka Chowdhary ◽

Stephan Dilchert ◽

...

Keyword(s):

Large Scale ◽

Wearable Sensors ◽

Wearable Devices ◽

Temperature Sensors ◽

Physiological Data ◽

High Temporal Resolution ◽

The Novel ◽

Illness Onset ◽

Novel Coronavirus ◽

Peripheral Temperature

Abstract Elevated core temperature constitutes an important biomarker for COVID-19 infection; however, no standards currently exist to monitor fever using wearable peripheral temperature sensors. Evidence that sensors could be used to develop fever monitoring capabilities would enable large-scale health-monitoring research and provide high-temporal resolution data on fever responses across heterogeneous populations. We launched the TemPredict study in March of 2020 to capture continuous physiological data, including peripheral temperature, from a commercially available wearable device during the novel coronavirus pandemic. We coupled these data with symptom reports and COVID-19 diagnosis data. Here we report findings from the first 50 subjects who reported COVID-19 infections. These cases provide the first evidence that illness-associated elevations in peripheral temperature are observable using wearable devices and correlate with self-reported fever. Our analyses support the hypothesis that wearable sensors can detect illnesses in the absence of symptom recognition. Finally, these data support the hypothesis that prediction of illness onset is possible using continuously generated physiological data collected by wearable sensors. Our findings should encourage further research into the role of wearable sensors in public health efforts aimed at illness detection, and underscore the importance of integrating temperature sensors into commercially available wearables.

Download Full-text

Monitoring and Predicting Occupant’s Sleep Quality by Using Wearable Device OURA Ring and Smart Building Sensors Data (Living Laboratory Case Study)

Buildings ◽

10.3390/buildings11100459 ◽

2021 ◽

Vol 11 (10) ◽

pp. 459

Author(s):

Elena Malakhatka ◽

Anas Al Al Rahis ◽

Osman Osman ◽

Per Lundqvist

Keyword(s):

Sleep Quality ◽

Ambient Air ◽

Wearable Devices ◽

Wearable Device ◽

Physiological Data ◽

Smart Meters ◽

Smart Building ◽

Good Potential ◽

Indoor Comfort

Today’s commercially-off-the-shelf (COST) wearable devices can unobtrusively capture several important parameters that may be used to measure the indoor comfort of building occupants, including ambient air temperature, relative humidity, skin temperature, perspiration rate, and heart rate. These data could be used not only for improving personal wellbeing, but for adjusting a better indoor environment condition. In this study, we have focused specifically on the sleeping phase. The main purpose of this work was to use the data from wearable devices and smart meters to improve the sleep quality of residents living at KTH Live-in-Lab. The wearable device we used was the OURA ring which specializes in sleep monitoring. In general, the data quality showed good potential for the modelling phase. For the modelling phase, we had to make some choices, such as the programming language and the AI algorithm, that was the best fit for our project. First, it aims to make personal physiological data related studies more transparent. Secondly, the tenants will have a better sleep quality in their everyday life if they have an accurate prediction of the sleeping scores and ability to adjust the built environment. Additionally, using knowledge about end users can help the building owners to design better building systems and services related to the end-user’s wellbeing.

Download Full-text

Research On human motion recognition algorithm based on WLD

International Journal Of Engineering And Computer Science ◽

10.18535/ijecs/v10i8.4615 ◽

2021 ◽

Vol 10 (8) ◽

pp. 25390-25393

Author(s):

SUN Qiu Feng ◽

LI Xia

Keyword(s):

Bayesian Method ◽

Rapid Development ◽

Wearable Devices ◽

Recognition Algorithm ◽

Human Motion ◽

The State ◽

Physiological Data ◽

Comparative Effect ◽

Human Motion Recognition ◽

Intelligent Technology

With the rapid development of intelligent technology,People’s lives have gradually entered the era of information and intelligentce,Wearable devices are becoming more and more popular,it is easier to use sensors to obtain data,even physiological data,from human body.When large amounts of data are collected by sensors,we can analyze and model them.the values of each characteristic are used to judge the user’s state,then according to the state we can provide users with more accurate and convenient services. In this paper,the data collected by different sensors are used to establish a prediction model and analyze the comparative effect of different recognition algorithms on the test data. The results of the experiment shows that the Bayesian method based on WLD identities the state of the human body better.

Download Full-text