Research Article
Physical Violence Detection with Movement Sensors
@INPROCEEDINGS{10.1007/978-3-030-00557-3_20, author={Liang Ye and Le Wang and Peng Wang and Hany Ferdinando and Tapio Sepp\aa{}nen and Esko Alasaarela}, title={Physical Violence Detection with Movement Sensors}, proceedings={Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings}, proceedings_a={MLICOM}, year={2018}, month={10}, keywords={Physical violence detection Activity recognition Movement sensor}, doi={10.1007/978-3-030-00557-3_20} }- Liang Ye
Le Wang
Peng Wang
Hany Ferdinando
Tapio Seppänen
Esko Alasaarela
Year: 2018
Physical Violence Detection with Movement Sensors
MLICOM
Springer
DOI: 10.1007/978-3-030-00557-3_20
Abstract
With the development of movement sensors, activity recognition becomes more and more popular. Compared with daily-life activity recognition, physical violence detection is more meaningful and valuable. This paper proposes a physical violence detecting method. Movement data of acceleration and gyro are gathered by role playing of physical violence and daily-life activities. Time domain features and frequency domain ones are extracted and filtered to discribe the differences between physical violence and daily-life activities. A specific BPNN trained with the L-M method works as the classifier. Altogether 9 kinds of activities are involved. For 9-class classification, the average recognition accuracy is 67.0%, whereas for 2-class classification, i.e. activities are classified as violence or daily-life activity, the average recognition accuracy reaches 83.7%.
