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Concentration level monitoring in education and healthcare

Authors:
  • ASIET,Kalady
  • Sultan Qaboos University (Adjunct)

Abstract

During the learning process, whether students remain attentive throughout the session influences their learning capability. If teachers can identify whether students are attentive they can be notified to remain focused, thus resulting in improving their learning capability. Traditional methods require, teachers observe students’ facial expressions to identify whether they are attentive during the session. However, this method is often inaccurate and increases the burden on teachers. The number of Alzheimer's patient and resulting deaths is increasing every year mainly due to delay in the early detection so as to take necessary measures and treatments to overcome it. With the development of electroencephalography (EEG) detection tools, a window has opened for developing an effective equipment to aid this cause.
Conclusion: Tai Chi exercise as an adjunctive treatment
may potentially improve smooth ventilating blood, smooth
emotion, strong bones and strong bones of Cervical
Spondylosis patients as well as quality of life. Tai Chi
exercise could be tentatively prescribed for Neck Type Cer-
vical Spondylosis in combination with the conventional
rehabilitation program to quicken the process of recovery.
Acknowledgements: This work was supported by The
Ministry of education of Humanities and Social Science
project (17YJA890025); The Fundamental Research Funds
for the Central Universities (WUT: 2018VI014).
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Concentration level monitoring in
education and healthcare
Abraham Varghese
1
; Ali Al Musawi
2
; Sunil Jacob
3
; Jibin Lukose
3
1
Information Technology Department, Higher college of Technology,
Muscat, Sultanate of Oman;
2
Department of Instructional and Learning
Technologies (ILT), Sultan Qaboos University, Muscat, Oman;
3
SCMS
Centre for Robotics, SCMS School of Engineering and Technology,
Ernakulam, Kerala, India
Background: During the learning process, whether stu-
dents remain attentive throughout the session influences
their learning capability. If teachers can identify whether
students are attentive they can be notified to remain
focused, thus resulting in improving their learning capabil-
ity. Traditional methods require, teachers observe students
facial expressions to identify whether they are attentive
during the session. However, this method is often inaccu-
rate and increases the burden on teachers. The number of
Alzheimer's patient and resulting deaths is increasing every
year mainly due to delay in the early detection so as to
take necessary measures and treatments to overcome it.
With the development of electroencephalography (EEG)
detection tools, a window has opened for developing an
effective equipment to aid this cause.
Objective: The aim is to develop a device that can check
the concentration level, memory capacity and arithmetic
levels of students and for the early detection of the Alzhei-
mer's disease. Teachers can put on remedial methods for
weaker students by offering them with special attention and
care. Early detection of Alzheimer's disease will help in
taking necessary measured to get cured or control the
extent of it.
Methods: Every subject undergoes a session of trials for
computing the subjectspecific threshold to assess his/her
attention and concentration level to perform specified tasks.
The subject will be sitting in an upright posture wearing
the EEG device, presented with the some objects or a sheet
of patterns. Session has 3 phases such as preparation, con-
centration and relaxed phases. During idle phase active
concentration to object or point on the pattern is avoided.
In the concentration phase, user actively concentrated at a
specified object or part of the pattern. In Relaxed phase
subjects are free to divert their focus from the objects and
patterns. Analysis of the data was done to estimate the val-
ues of the EEG signal extracted during the active concen-
tration period. The average data of the trials is taken as the
threshold. The threshold and the extracted raw data are
compared and analyzed. The resulting data is used for the
brain activity analysis which would help in the effective
detection of Alzheimer's in its early stages.
Results: Effective EEG signal extraction is achieved. Effi-
ciently analyzed concentration levels of students thereby
helping the staffs to analyse their concentration levels and
employing necessary measures to enhance the brain activity
and concentration skills of the students. In healthcare early
detection of the Alzheimer's was very supportive to help
the patient and provide necessary treatment and care at the
early stages itself.
Conclusion: A more user friendly, compact and better por-
table system will be designed such that it could be easily
used in education institutions. The easy portable design
will also be very helpful in hospitals aiding the patient to
move around and interact by wearing the device.
061
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Study and design of physical health
management system for civil servants in Hubei
province of China
Qi Luo
College of Sports Engineering and Information Technology, Wuhan Sports
University, Wuhan, 430079, China
Objectives: The purpose of this paper is to construct a
complete civil servant health assessment index system
(adding psychological and social adaptation indicators), and
to take civil servants in Hubei province as the research
object, aiming at comprehensive, objective and accurate
evaluation of the physical health status of civil servants in
Hubei province. On the basis of seizing the factors affect-
ing the health of the civil servants, we searched the opin-
ions of the collective health experts, designed targeted
exercise prescriptions, and finally developed and designed
the Hubei province civil servant health management inte-
grated system (including database, expert database and
website), which support for the practice of promoting the
health of civil servants. This project is the need to imple-
ment the scientific concept of development, strengthen the
quality of civil servants, and build a civil servant team that
is lean, efficient, diligent, honest and pragmatic, so that
civil servants can understand their physical health and seize
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ABSTRACTS
... ere have been many attempts to measure students' concentration levels using various methods, such as taking skin temperature [10], recognizing visual attention and students' emotions [11], and detecting electroencephalogram (EEG) signals [12][13][14]. However, these methods often do not work well in online classes because teachers cannot promptly interact with each student. ...
Article
Full-text available
The COVID-19 pandemic heavily influenced human life by constricting human social activity. Following the spread of the pandemic, humans did not have a choice but to change their lifestyles. There has been much change in the field of education, which has led to schools hosting online classes as an alternative to face-to-face classes. However, the concentration level is lowered in the online learning class, and the student’s learning rate decreases. We devise a framework for recognizing and estimating students’ concentration levels to help lecturers. Previous studies have a limitation in that they classified attention levels using only discrete states. Due to the partial information from discrete states, the concentration levels could not be recognized well. This research aims to estimate more subtle levels as specified states by using a minimum amount of body movement data. The deep neural network is used to continuously recognize the human concentration model, and the concentration levels can be predicted and estimated by the Kalman filter. Using our framework, we successfully extracted the concentration levels, which can aid lecturers and can be expanded to other areas. To implement the framework, we recruited participants to take online classes. Data were collected and preprocessed using pose points, and an accuracy of 90.62 % was calculated by predicting the concentration level using the framework. Furthermore, the concentration level was approximated based on the Kalman filter. We found that webcams can be used to quantitatively measure student concentration when conducting online classes. Our framework is a great help for instructors to measure concentration levels, which can increase the learning efficiency. As a future work of this study, if emotion data and skin thermal data are comprehensively considered, a student’s concentration level can be measured more precisely.
... There have been many attempts to measure students' concentration levels using various methods such as taking skin temperature [1], recognizing visual attention, and detecting Electroencephalogram (EEG) signals [2][3][4]. However, these attempts lack in detail since their concentration levels were classified as discrete states. ...
Preprint
Full-text available
One of the major tasks in online education is to estimate the concentration levels of each student. Previous studies have a limitation of classifying the levels using discrete states only. The purpose of this paper is to estimate the subtle levels as specified states by using the minimum amount of body movement data. This is done by a framework composed of a Deep Neural Network and Kalman Filter. Using this framework, we successfully extracted the concentration levels, which can be used to aid lecturers and expand to other areas.
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