Detecting Learning Activities with MEMS Sensors and Machine Learning

Virtual Brief Paper ID: 43593
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    KAI LI
    Toyohashi University of Technology

Abstract: In this study, we describe a system for detecting learning activity using micro electro mechanical system (MEMS) sensors. The MEMS sensor contains gyro, accelerometer, and compass sensors. The activity sensor data are transferred to a computer via Bluetooth. To recognize learning activity, we use a machine learning tool-WEKA for the automatic extraction of learning activity classification. The accuracy of activity recognition shows 90% in an experiment. With this system, we expect it could be used in large-scale classroom lecture and e-learning to help teachers and parents to monitor or analyze learners’ activity for better learning.

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