Context Analysis and Estimation of Mobile Users by using Bio-signals and Sensor Data

The sensor data obtained from mobile and wearable devices are useful to analyze and estimate user's context, but also user's bio-signals are, because they may reflect user's psychological aspects in the corresponding context. Therefore, in this paper, we focus on context analysis and...

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Bibliographic Details
Published in2019 IEEE 8th Global Conference on Consumer Electronics (GCCE) pp. 263 - 266
Main Authors Shimizu, Hiromi, Suganuma, Mutsumi, Kameyama, Wataru
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2019
Subjects
Acceleration
Angular velocity
bio-signals
context analysis
context estimation
Estimation
Machine learning
Radio frequency
Rail to rail inputs
sensor data
Supervised learning
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Summary:The sensor data obtained from mobile and wearable devices are useful to analyze and estimate user's context, but also user's bio-signals are, because they may reflect user's psychological aspects in the corresponding context. Therefore, in this paper, we focus on context analysis and estimation of mobile users by using bio-signals and sensor data of mobile devices. For the analysis and estimation, various machine learning methods are applied to classify the data into pre-defined six contexts. The evaluation shows that Gradient Boosting Decision Tree achieves the highest classification accuracy of about 80% in supervised methods, and Sparse Representation-based Classification achieves more than 90% accuracy. The results suggest that the context analysis and estimation can be done accurately by using bio-signals and sensor data.
DOI:10.1109/GCCE46687.2019.9015475