TY - GEN
T1 - College Student Activity Recognition from Smartwatch Dataset
AU - de Clairval, Arthur
AU - Schuler, Laurent Alain Erwin
AU - Rellier, Mathis Franck
AU - Irawan, Mohammad Isa
AU - Mukhlash, Imam
AU - Iqbal, Mohammad
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - We present a framework to recognize college student activities by monitoring their movements from the smartwatch. The goal of this work is to support smart educational systems by giving daily college student activity info. The proposed framework comprises a way to collect the college student trajectories and apply machine learning models to recognize their activities. Moreover, the proposed framework collects additional information from the smartwatch, which can elevate the accuracy of recognition. In the experiments, we observed three college students for 2 months in Department of Mathematics, Institut Teknologi Sepuluh Nopember. As a result, we introduce a benchmark dataset for college student activity recognition, namely, CSARD. Further, we compiled several machine learning models on CSRAD. The experiment results showed the highest accuracy coming from the random forest model with all features.
AB - We present a framework to recognize college student activities by monitoring their movements from the smartwatch. The goal of this work is to support smart educational systems by giving daily college student activity info. The proposed framework comprises a way to collect the college student trajectories and apply machine learning models to recognize their activities. Moreover, the proposed framework collects additional information from the smartwatch, which can elevate the accuracy of recognition. In the experiments, we observed three college students for 2 months in Department of Mathematics, Institut Teknologi Sepuluh Nopember. As a result, we introduce a benchmark dataset for college student activity recognition, namely, CSARD. Further, we compiled several machine learning models on CSRAD. The experiment results showed the highest accuracy coming from the random forest model with all features.
KW - Activity recognition
KW - College student
KW - GPS data
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85200660412&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-2136-8_20
DO - 10.1007/978-981-97-2136-8_20
M3 - Conference contribution
AN - SCOPUS:85200660412
SN - 9789819721351
T3 - Springer Proceedings in Mathematics and Statistics
SP - 271
EP - 281
BT - Applied and Computational Mathematics - ICoMPAC 2023
A2 - Adzkiya, Dieky
A2 - Fahim, Kistosil
PB - Springer
T2 - 8th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2023
Y2 - 30 September 2023 through 30 September 2023
ER -