Students Activity Recognition by Heart Rate Monitoring in Classroom using K-Means Classification
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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.
A. Juul and A. E. Jeukendrup, "Heart rate monitoring: Applications and limitations,” Sport Medicine, vol. 33, pp. 517Á538., 2003.
A. K. Joshi, A. Tomar, and M. Tomar, "A Review Paper on Analysis of Electrocardiograph (ECG) Signal for the Detection of Arrhythmia Abnormalities,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 3, no. 10, pp. 12466–12475, 2014, doi: https://doi.org/10.15662/ijareeie.2014.0310028.
F. Sartor, J. Gelissen, R. van Dinther, D. Roovers, G. B. Papini, and G. Coppola, "Wrist-worn optical and chest strap heart rate comparison in a heterogeneous sample of healthy individuals and in coronary artery disease patients,” BMC Sports Science, Medicine & Rehabilitation, vol. 10, no. 1, pp. 10, May 2018, doi: https://doi.org/10.1186/s13102-018-0098-0.
P. J. Soh, G. A. E. Vandenbosch, M. Mercuri, and D. M. M.-P. Schreurs, "Wearable Wireless Health Monitoring Systems (WWHMS): Current Developments, Challenges and Future Trends,” IEEE Microwave Magazine, vol. 16, no. 4, pp. 55–70, 2015.
A. Lele, "Internet of Things (IoT),” in Disruptive Technologies for the Militaries and Security, vol. 132, Singapore: Springer, 2019, pp. 187–195.
A. W. Gorny, S. J. Liew, C. S. Tan, and F. Müller-Riemenschneider, "Fitbit Charge HR Wireless Heart Rate Monitor: Validation Study Conducted Under Free-Living Conditions,” JMIR mHealth and uHealth, vol. 5, no. 10, pp. e157, 2017, doi: https://doi.org/10.2196/mhealth.8233.
B. Xie and H. Minn, "Real-time sleep apnea detection by classifier combination,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 3, pp. 469–477, 2012, doi: https://doi.org/10.1109/TITB.2012.2188299.
I. Mori et al., "Association Between Heart Rate and Reversibility of the Symptom, Refractoriness to Palliative Treatment, and Survival in Dyspneic Cancer Patients,” Journal of Pain and Sympton Management, vol. In Press, Corrected Proof, Feb. 2020, doi: https://doi.org/10.1016/j.jpainsymman.2020.02.011.
H. Bouchery-Bardet et al., "Heart rate and risk of death among patients with Pulmonary Hypertension: A 12-lead ECG analysis,” Respiratory Medicine, vol. 132, pp. 42–49, Nov. 2017, doi: https://doi.org/10.1016/j.rmed.2017.09.008.
M. Adnot, T. Dee, V. Katz, and Wyckoff, "Teacher Turnover, Teacher Quality, and Student Achievement in DCPS,” Educational Evaluation and Policy Analysis, vol. 39, no. 1, pp. 54–76, 2017, doi: https://doi.org/10.3102/0162373716663646.
D. Castaneda, A. Esparza, M. Ghamari, C. Soltanpur, and H. Nazeran, "A review on wearable photoplethysmography sensors and their potential future applications in health care,” International Journal of Biosensors & Bioelectronics, vol. 4, no. 4, pp. 195–202, 2018, doi: https://doi.org/10.15406/ijbsbe.2018.04.00125.
H. N. Saha and S. Auddy, "Health monitoring using Internet of Things (IoT),” presented at the 2017, 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), Bangkok, Thailand, Aug. 2017, doi: 10.1109/IEMECON.2017.8079564.
M. Lemay, M. Bertschi, J. Sola, P. Renevey, J. Parak, and I. Korhonen, "Application of Optical Heart Rate Monitoring,” in Wearable Sensors : Fundamentals, Implementation and Applications, Academic Press, 2014, pp. 105–129.
Y. Shu, C. Li, Z. Wang, W. Mi, Y. Li, and T.-L. Ren, "A pressure sensing system for heart rate monitoring with polymer-based pressure sensors and an anti-interference post processing circuit,” Sensors, vol. 15, no. 2, pp. 3224–3235, 2015, doi: https://doi.org/10.3390/s150203224.
W. Lefebvre-Ulrikso, G. D. Costa, L. Rigutti, and I. Blum, "Data Mining,” in Atom Probe Tomography Put Theory Into Practice, 2016, pp. 279–317.
M. P. G. Martins, V. L. Miguéis, D. S. B. Fonseca, and A. Alves, "A Data Mining Approach for Predicting Academic Success – A Case Study,” in Information Technology and Systems, Jan. 2019, vol. 918, pp. 45–56, doi: https://doi.org/10.1007/978-3-030-11890-7_5.
M. Abdar, S. R. N. Kalhori, T. Sutikno, I. M. I. Subroto, and G. Arji, "Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases,” IJECE, vol. 5, no. 6, pp. 1569-1576, Dec. 2015.
S. Balli, E. A. Sag˘bas, and M. Peker, "Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm,” Measurement and Control (United Kingdom), vol. 52, no. 1–2, pp. 37–45, 2019, doi: https://doi.org/10.1177/0020294018813692.
S. S. Bashar, Md. S. Miah, A. H. M. Z. Karim, Md. A. Al Mahmud, and Z. Hasan, "A Machine Learning Approach for Heart Rate Estimation from PPG Signal using Random Forest Regression Algorithm,” presented at the 2nd International Conference on Electrical, Computer and Communication Engineering, ECCE 2019, 2019, doi: https://doi.org/10.1109/ECACE.2019.8679356.
S. H. Mujawar and P. R. Devale, "Prediction of Heart Disease using Modified K-means and by using Naive Bayes,” International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization), vol. 3, no. 10, Oct. 2015, doi: 10.15680/IJIRCCE.2015. 0310123.
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