Experimental Evaluation on Machine Learning Techniques for Human Activities Recognition in Digital Education Context

Leitão, Gabriel; Colonna, Juan Gabriel; Ribeiro, Erick Elisson Hosana; Barreto, Raimundo; Araujo, Thierry-Yves; Martins, Anny Caroline Castelo Branco; Koster, Andrew; Koch, Fernando

doi:10.1007/978-3-319-39672-9_9

Cited by 2 publications

(1 citation statement)

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“…Few researches have addressed the activity monitoring focused on academic performance using wearables devices. For example, Leitão et al [23] presented an experimental evaluation of machine learning supervised techniques in human activity recognition where smartphones sensors data are collected with the overall goal of recognizing activities to identify students with attention deficit or hyperactivity problems based on three activities: walking, standing, and sitting.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Student Activities with Smartwatches: On the Academic Performance Enhancement

Herrera-Alcántara

Barrera-Animas

González-Mendoza

et al. 2019

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%