scholarly journals An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial Accelerometer Wrist-Band

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 613 ◽  
Author(s):  
Saeed Mehrang ◽  
Julia Pietilä ◽  
Ilkka Korhonen
Keyword(s):  
Heart Rate ◽  
Random Forest ◽  
Activity Recognition ◽  
Random Forest Classifier ◽  
Triaxial Accelerometer ◽  
Heart Rate Monitoring

scholarly journals A Robust Random Forest-Based Approach for Heart Rate Monitoring Using Photoplethysmography Signal Contaminated by Intense Motion Artifacts

Sensors ◽  
2017 ◽  
Vol 17 (2) ◽  
pp. 385 ◽  
Author(s):  
Yalan Ye ◽  
Wenwen He ◽  
Yunfei Cheng ◽  
Wenxia Huang ◽  
Zhilin Zhang
Keyword(s):  
Heart Rate ◽  
Random Forest ◽  
Motion Artifacts ◽  
Heart Rate Monitoring

scholarly journals Students Activity Recognition by Heart Rate Monitoring in Classroom using K-Means Classification

2020 ◽  
Vol 6 (1) ◽  
pp. 46
Author(s):  
Hadi Helmi Md Zuraini ◽  
Waidah Ismail ◽  
Rimuljo Hendradi ◽  
Army Justitia
Keyword(s):  
Heart Rate ◽  
Activity Recognition ◽  
Student Behavior ◽  
Exercise Heart Rate ◽  
Rate Measurement ◽  
Teaching Session ◽  
Heart Rate Monitoring ◽  
Student Reaction ◽  
Heart Rate Measurement

Background: Heartbeat playing the main roles in our life. With the heartbeat, the anxiety level can be known. Most of the heartbeat is used in the exercise. Heart rate measurement is unique and uncontrollable by any human being.Objective: This research aims to learn student’s actions by monitoring the heart rate. In this paper, we are measuring the student reaction and action in classroom can give impact on teacher’s way of delivery when in the teaching session. In monitoring, student’s behavior may give feedback whether the teaching session have positive or negative outcome.Methods: The method we use is K-Means algorithm. Firstly, we need to know the student’s normal heartbeat as benchmark. We used Hexiware for collecting data from students’ hear beat. We perform the classification where K is benchmark students’ heartbeat. K-Means algorithm performs classification of the heart rate measurement of students.Results: We did the testing for five students in different subjects. It shows that all students have anxiety during the testing and presentation. Its consistency because we tested 5 students with mixes activities in the classroom, where the student has quiz, presentation and only teaching.Conclusion: Heart rate during studying in the classroom can change the education world in improving the efficiency of knowledge transfer between student and teacher. This research may act as basic way in monitoring student behavior in the classroom. We have tested for 5 students. Three students have their anxiety in classroom during the exam, presentation, and question. Two students have normal rate during the seminar and lecturer. The drawback, Hexiware is capturing average of ten minutes and tested in different classes and students. In future, we need just measure one student for all the subjects and Hexiware need to configure in one minute. 

Emotional Experiences of Students in the Classroom

2010 ◽  
Vol 15 (2) ◽  
pp. 142-151 ◽  
Author(s):  
Wondimu Ahmed ◽  
Greetje van der Werf ◽  
Alexander Minnaert
Keyword(s):  
Heart Rate ◽  
Emotional Response ◽  
Task Difficulty ◽  
Physiological Reactivity ◽  
Emotional Experiences ◽  
Heart Rate Monitoring ◽  
Coding Scheme ◽  
Stimulated Recall ◽  
Response Systems ◽  
The Relationship

In this article, we report on a multimethod qualitative study designed to explore the emotional experiences of students in the classroom setting. The purpose of the study was threefold: (1) to explore the correspondence among nonverbal expressions, subjective feelings, and physiological reactivity (heart rate changes) of students’ emotions in the classroom; (2) to examine the relationship between students’ emotions and their competence and value appraisals; and (3) to determine whether task difficulty matters in emotional experiences. We used multiple methods (nonverbal coding scheme, video stimulated recall interview, and heart rate monitoring) to acquire data on emotional experiences of six grade 7 students. Concurrent correspondence analyses of the emotional indices revealed that coherence between emotional response systems, although apparent, is not conclusive. The relationship between appraisals and emotions was evident, but the effect of task difficulty appears to be minimal.

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