Unsupervised Anomaly Detection Using Light Switches for Smart Nursing Homes
Journal
Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016
Pages
803-810
Date Issued
2016
Author(s)
Abstract
Anomaly detection plays a critical role in various smart living scenarios. However, achieving effective detection while not imposing burdens on users is never an easy task. To solve this problem, an unsupervised anomaly detection algorithm using light switches is proposed. By using an unsupervised approach, care takers in nursing homes do not need to label the collected data. By using information generated by smart switches, senior citizens are not forced to use wearables, change the battery or feel privacy invasion from cameras. Our solution adopts the statistical-based algorithm based on expectation maximization (EM). By adding constrains to reduce the high variances of the mixture model and recursively removing the extremely-low probability data from the model, a more accurate mixture model can be constructed. Our experiments in a real apartment show that the false alarm rate can be reduced by at least 56% compared to the existing cluster-based algorithms when the targeted miss detection rates are low.
Type
conference paper