Anomaly Detection for Human Home Activities Using Pattern Based Sequence Classification
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2023.17.1.4Keywords:
anomaly detection, classification, elderly, home activities, sequence patternAbstract
In most countries, the old-age people population continues to rise. Because young adults are busy with their work engagements, they have to let the elderly stay at home alone. This is quite dangerous, as accidents at home may happen anytime without anyone knowing. Although sending elderly relatives to an elderly care center or hiring a caregiver are good solutions, they may not be feasible since it may be too expensive over a long-term period. The behavior patterns of elderly people during daily activities can give hints about their health condition. If an abnormal behavior pattern can be detected in advance, then precautions can be taken at an early stage. Previous studies have suggested machine learning techniques for such anomaly detection but most of the techniques are complicated. In this paper, a simple model for detecting anomaly patterns in human activity sequences using Random forest (RF) and K-nearest neighbor (KNN) classifiers is presented. The model was implemented on a public dataset and it showed that the RF classifier performed better, with an accuracy of 85%, compared to the KNN classifier, which achieved 73%.
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References
United Nations , World Population Ageing 2015, URL Link:
http://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2015_Report.pdf , (2015)
Bourobou, Serge T.M. & Yoo, Younghwan, User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm, Sensors, 15(5), pp. 11953-11971, 2015.
Zhu, X. & Liu, Z., Human behavior clustering for anomaly detection. Front. Comput. Sci. China 5, 279, 2011.
Zhu, Xudong, Liu, Zhijing & Zhang, Juehui, Human Activity Clustering for Online Anomaly Detection, Journal of Computers, 6(6), pp. 1071-1079, 2011.
Chengfei Wu & Zixuan Cheng, A Novel Detection Framework for Detecting Abnormal Human Behavior, Mathematical Problems in Engineering, 2020(1), pp. 1-9 , 2020.
P. Antonakaki, D. Kosmopoulos & S. Perantonis, Detecting abnormal human behaviour using multiple cameras, Signal Processing, 89(9), pp. 1723-1738, 2009.
Enshaeifar S., Zoha A., Skillman S., Markides A., Acton S.T., Elsaleh T., Kenny M., Rostill H., Nilforooshan R. & Barnaghi P., Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia. PLoS ONE 14(1), e0209909, Jan 2019.
Hui Huang, Xian L & Ye Sun, A triboelectric motion sensor in wearable body sensor network for human activity recognition, Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Annual Conference, pp. 4889-4892, 2016.
A. S. A. Sukor, A. Zakaria & N. A. Rahim, Activity recognition using accelerometer sensor and machine learning classifiers, IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), Penang, Malaysia, pp. 233-238., 2018.
J. Yin, Q. Yang & J. J. Pan, Sensor-Based Abnormal Human-Activity Detection, IEEE Transactions on Knowledge and Data Engineering, 20(8), pp. 1082-1090, 2008.
Min-Cheol Kwon & Sunwoong Choi, Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch, Wireless Communications and Mobile Computing, 2018, pp. 1-9, June 2018
Damla, A. & Bouchachia, A., Activity Recognition and Abnormal Behavior Detection using Recurrent Neural Networks, Procedia Computer Science, 110, pp. 86-93, 2017.
Hui-Huang Hsu & Chien-Chen Chen, RFID-Based Human Behavior Modeling and Anomaly Detection for Elderly Care, Mobile Information Systems 6(4), pp. 341-354, Jan 2010.
Poh, S., Tan, Y., Cheong, S., Ooi, C. & Tan, W., Anomaly detection on in-home activities data based on time interval, Indonesian Journal of Electrical Engineering and Computer Science, 15(2), p.778 ? 785, 2019.
S. C. Poh, Y. F. Tan, S. N. Cheong, C. P. Ooi & W. H. Tan, Abnormal detection for home activity based on sequence pattern,International Journal of Technology 10(7), pp. 1276-1285, 2019.
Poh, S., Tan, Y., Guo, X., Cheong, S., Ooi, C. & Tan, W., LSTM and HMM comparison for home activity anomaly detection, IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference. Chengdu, pp.15-17, 2019.
D. Cook, Aruba, Washington State University, Center for Advanced Studies in Adaptive Systems, URL Link: http://casas.wsu.edu/datasets/, (2011)
Forkan, A., Khalil, I., Tari, Z., Foufou, S. & Bouras, A., A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living. Pattern Recognition, 48(3), pp.628-641, 2015.
Erfanmanesh M., Tahayori H. & Visconti A., Elderly Action Prediction and Anomalous Activity Detection in Smart Homes through Profiling Residents? Behavior, Modern Care Journal, 16(3):e94661, Aug 2019.
Hoque, E., Dickerson, R., Preum, S., Hanson M. & Barth, A., Stankovic, J., Holmes: A Comprehensive Anomaly Detection System for Daily In-home Activities, International Conference on Distributed Computing in Sensor Systems, IEEE, Fortaleza, Brazil, pp. 40-51, 2015.
Car J.L., Pelz V., Lez G., Ferndez M., Alvarez E. & Dz G., Unsupervised abnormal human behaviour detection using acceleration data, Stud Health Technol Inform,189, pp.65-70, 2013.
Jill Seladi-Schulman, What Are the 3 P?s of Diabetes? , Healthline, URL Link: https://www.healthline.com/health/diabetes/3-ps-of-diabetes , (June 18, 2020)
Tony Yiu, Understanding Random Forest, Medium, URL Link: https://towardsdatascience.com/understanding-random-forest-58381e0602d2 , (Jun 12, 2019).
Onel Harrison, Machine Learning Basics with the K-Nearest Neighbors Algorithm, Medium, URL Link: https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761, (Sep 11, 2018)
Aniruddha Bhandari, Everything you Should Know about Confusion Matrix for Machine Learning, Analytics Vidhya, URL Link: https://www.analyticsvidhya.com/blog/2020/04/confusion-matrix-machine-learning/, (April 17, 2020)
