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Monitoring student activities with smartwatches: On the academic performance enhancement

Herrera-Alcántara, Oscar; Barrera-Animas, Ari Yair; González-Mendoza, Miguel; Castro-Espinoza, Félix

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Authors

Oscar Herrera-Alcántara

Ari Yair Barrera-Animas

Miguel González-Mendoza

Félix Castro-Espinoza



Abstract

Motivated by the importance of studying the relationship between habits of students and their academic performance, daily activities of undergraduate participants have been tracked with smartwatches and smartphones. Smartwatches collect data together with an Android application that interacts with the users who provide the labeling of their own activities. The tracked activities include eating, running, sleeping, classroom-session, exam, job, homework, transportation, watching TV-Series, and reading. The collected data were stored in a server for activity recognition with supervised machine learning algorithms. The methodology for the concept proof includes the extraction of features with the discrete wavelet transform from gyroscope and accelerometer signals to improve the classification accuracy. The results of activity recognition with Random Forest were satisfactory (86.9%) and support the relationship between smartwatch sensor signals and daily-living activities of students which opens the possibility for developing future experiments with automatic activity-labeling, and so forth to facilitate activity pattern recognition to propose a recommendation
system to enhance the academic performance of each student.

Citation

Herrera-Alcántara, O., Barrera-Animas, A. Y., González-Mendoza, M., & Castro-Espinoza, F. (2019). Monitoring student activities with smartwatches: On the academic performance enhancement. Sensors, 19(7), 1605. https://doi.org/10.3390/s19071605

Journal Article Type Article
Acceptance Date Mar 26, 2019
Online Publication Date Apr 3, 2019
Publication Date Apr 1, 2019
Deposit Date Feb 18, 2020
Journal Sensors
Print ISSN 1424-8220
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 19
Issue 7
Pages 1605
DOI https://doi.org/10.3390/s19071605
Keywords human activity recognition, smartwatch sensors, supervised classification
Public URL https://uwe-repository.worktribe.com/output/5130089

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