Research Article
Abnormal Behavior Detection Based on Smartphone Sensors
@INPROCEEDINGS{10.1007/978-3-319-77818-1_19, author={Dang-Nhac Lu and Thuy-Binh Tran and Duc-Nhan Nguyen and Thi-Hau Nguyen and Ha-Nam Nguyen}, title={Abnormal Behavior Detection Based on Smartphone Sensors}, proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 6th International Conference, ICCASA 2017, and 3rd International Conference, ICTCC 2017, Tam Ky, Vietnam, November 23-24, 2017, Proceedings}, proceedings_a={ICCASA \& ICTCC}, year={2018}, month={3}, keywords={Activity recognition Behavior recognition Detecting behavior PSO algorithm}, doi={10.1007/978-3-319-77818-1_19} }- Dang-Nhac Lu
Thuy-Binh Tran
Duc-Nhan Nguyen
Thi-Hau Nguyen
Ha-Nam Nguyen
Year: 2018
Abnormal Behavior Detection Based on Smartphone Sensors
ICCASA & ICTCC
Springer
DOI: 10.1007/978-3-319-77818-1_19
Abstract
There are a lot of applications were developed to take advance of smartphone sensors for utilizing the personal services such as health-care, walk-counting, routing etc. Users behavior analysis is attracted a lot of researches interested with various approaches. We proposed a novel framework to detect the abnormal driving behavior using smartphone sensors. It named Abnormal Behavior Detection System (ABDS). The system keep track the driver activities during he’s trip based on smartphone sensors. The Practice Swarm Optimization (PSO) algorithm is used to automatically select suitable features extracted from sensors data. The oriented accelerometer is used to detect activity. The abnormal behavior is collected and labeled then detection by Artificial Neural Network (ANN). The implementation shown the promising results in case of seven activities (stop, moving, acceleration, deceleration, turn left, turn right and U-turn) with 86.71% accuracy.
