TY - GEN
T1 - Context analysis and estimation of mobile users by using bio-signals and sensor data
AU - Shimizu, Hiromi
AU - Suganuma, Mutsumi
AU - Kameyama, Wataru
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by JSPS KAKENHI Grant Number JP15H01684 and JP19K11932
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
KW - Bio-signals
KW - Context analysis
KW - Context estimation
KW - Machine learning
KW - Sensor data
UR - http://www.scopus.com/inward/record.url?scp=85081984366&partnerID=8YFLogxK
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U2 - 10.1109/GCCE46687.2019.9015475
DO - 10.1109/GCCE46687.2019.9015475
M3 - Conference contribution
AN - SCOPUS:85081984366
T3 - 2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019
SP - 263
EP - 266
BT - 2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE Global Conference on Consumer Electronics, GCCE 2019
Y2 - 15 October 2019 through 18 October 2019
ER -