Abstract
Among many dangerous situations, the number of cases of violence has been growing recently. However, there is currently no research to recognize conditions such as assault. Therefore, this paper presents a VR (Violence-Recognition) model for recognition activity using LSTM. The VR model develops algorithms that can detect dangerous situations through processing and analysis of sensing data. Also, to improve accuracy by using the FFT algorithm for processing digital signals in combination with LSTM.
Keywords
- Smartphone
- Smartwatch
- Fusion sensing
- Abnormal detection
- LSTM
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References
Aziza O, Parkc EJ, Morid G, Robinovitch SN (2014) Distinguishing the causes of falls in humans using an array of wearable tri-axial accelerometers. Gait Posture 39:506–512
Bogomolov A, Lepri B, Pianesi F (2013) Happiness recognition from mobile phone data. In: BioMedCom 2013
Chittaranjan G, Blom J, Gatica-Perez D (2013) Mining large-scale smartphone data for personality studies. Pers Ubiquitous Comput 17(3):433–450
Pierleoni P, Pernini L, Belli A, Palma L (2014) An android-based heart monitoring system for the elderly and for patients with heart disease. Int J Telemed Appl 11
Geronimo D, Lopez AM, Sappa AD, Graf T (2010) Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans Pattern Anal Mach Intell 32(7):1239–1258
Lee S, Mase K (2002) Activity and location recognition using wearable sensors. IEEE Pervasive Comput 1:24–32
Randell C, Muller H (2000) Context awareness by analysing accelerometer data. In: The fourth international symposium on wearable computers, pp 175–176
Jahangiri A, Rakha HA (2015) Applying machine learning techniques to transportation mode recognition using mobile phone sensor data. IEEE Trans Intell Transp Syst 16(5):2406–2417
Anjum A, Ilyas MU (2013) Activity recognition using smartphone sensors. In: 2013 IEEE consumer communications and networking conference (CCNC), pp 914–919
Martín H, Bernardos AM, Iglesias J, Casar JR (2013) Activity logging using lightweight classification techniques in mobile devices. Pers Ubiquitous Comput 17(4):675–695
Lopez-Cuevas A, Medina-Perez MA, Monroy R, Rez-Marquez JER, Luis A (2018) FiToViz: a visualisation approach for real-time risk situation awareness. IEEE Trans Affect Comput, pp 372–373
Wu F, Zhao H, Zhao Y, Zhong H (2015) Development of a wearable-sensor-based fall detection system. Int J Telemed Appl, Art. no. 2
Hengduo L, Jun L, Yuan G, Yirui W (2017) Multi-glimpse LSTM with color-depth feature fusion for human detection. In: IEEE international conference on image processing (ICIP)
Acknowledgements
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00336, Platform Development of Multi-log based Multi-Modal Data Convergence Analysis and Situational Response).
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Kim, S., Nam, H., Park, H., Lee, YT., Yoon, Y. (2021). Activity-Recognition Model for Violence Behavior Using LSTM. In: Park, J.J., Fong, S.J., Pan, Y., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. Lecture Notes in Electrical Engineering, vol 715. Springer, Singapore. https://doi.org/10.1007/978-981-15-9343-7_75
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DOI: https://doi.org/10.1007/978-981-15-9343-7_75
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