TY - GEN
T1 - Conceptual group activity recognition model for classroom environments
AU - Choi, Jung In
AU - Yong, Hwan Seung
N1 - Funding Information:
This research was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (2012R1A1A2003764).
Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/11
Y1 - 2015/12/11
N2 - With the development of smartphones containing built-in sensors of various kinds, an increasing amount of research effort is being devoted to recognition using wearable devices. In this paper, we limit our research to personal activity recognition, which is important to efficiently accumulate sensor data. We propose 1) a method to recognize conceptual group activity, and 2) a big data model to analyze large amounts of streaming data. This study focuses on three activities in the classroom environment: Taking a Lesson, Presentation, and Discussion. In our experiments, the proposed recognition algorithm recorded an accuracy of over 96%. We used the big data programming model MapReduce to accumulate and analyze data, and stored the sensor data and the activity data in a big data repository. In future research, we plan to study group activity recognition in other environments, and design a big data streaming system for group activity recognition.
AB - With the development of smartphones containing built-in sensors of various kinds, an increasing amount of research effort is being devoted to recognition using wearable devices. In this paper, we limit our research to personal activity recognition, which is important to efficiently accumulate sensor data. We propose 1) a method to recognize conceptual group activity, and 2) a big data model to analyze large amounts of streaming data. This study focuses on three activities in the classroom environment: Taking a Lesson, Presentation, and Discussion. In our experiments, the proposed recognition algorithm recorded an accuracy of over 96%. We used the big data programming model MapReduce to accumulate and analyze data, and stored the sensor data and the activity data in a big data repository. In future research, we plan to study group activity recognition in other environments, and design a big data streaming system for group activity recognition.
KW - Big data processing
KW - Conceptual activity
KW - Group activity recognition
KW - Logical activity
KW - Physical activity
KW - Streaming data processing
UR - http://www.scopus.com/inward/record.url?scp=84964810445&partnerID=8YFLogxK
U2 - 10.1109/ICTC.2015.7354632
DO - 10.1109/ICTC.2015.7354632
M3 - Conference contribution
AN - SCOPUS:84964810445
T3 - International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015
SP - 658
EP - 661
BT - International Conference on ICT Convergence 2015
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 October 2015 through 30 October 2015
ER -