Abnormal behaviours identification for an elder's life activities using dissimilarity measurements
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Semantic anomaly detection in daily activities
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
An ontology-based approach to ADL recognition in smart homes
Future Generation Computer Systems
| Hi-index | 0.00 |
In this paper, we propose a method consists of two com- ponents, behavior patterns extraction and anomaly detec- tion algorithm in daily life. To begin with, sensor data are accumulated in a room environment and behavior de- scription labels are assigned for each data segment using HMM(Hidden Markov Model) and k-means method. An HMM is composed every day based on sensor data segments of the day. The behavior description label at each time seg- ment is determined by likelihood of the segment computed using the HMM. In anomaly detection step, typical behavior sequences are acquired using probabilistic density of behav- ior occurrence and behavior successive time. Each proba- bilistic density is composed based on accumulating labeled- data using Sequential Discounting Laplace Estimation and Sequential Discounting Expectation and Maximization al- gorithms. When a new datum comes, if typical behavior data change largely, the data is detected as anomaly. The proposed method is verified by a long-time activity detection sensor data taken at a house of elderly person.