Paper Info
Title
Monitoring Student Activities with Smartwatches: On the Academic Performance Enhancement.
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.
Year
DOI
Venue
2019
10.3390/s19071605
SENSORS
Keywords
Field
DocType
human activity recognition,smartwatch sensors,supervised classification
Recommender system,Android (operating system),Activities of daily living,Activity recognition,Accelerometer,Electronic engineering,Human–computer interaction,Student activities,Engineering,Random forest,Smartwatch
Journal
Volume
Issue
ISSN
19
7.0
1424-8220
Citations 
PageRank 
References 
2
0.36
0
Authors
4
Name
Order
Citations
PageRank
Oscar Herrera Alcántara153.23
Ari Yair Barrera-Animas220.36
Miguel González Mendoza342.46
Félix Castro-Espinoza420.36