Information Retrieval
Fuzzy Hamming Distance: A New Dissimilarity Measure
CPM '01 Proceedings of the 12th Annual Symposium on Combinatorial Pattern Matching
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
On Binary Similarity Measures for Handwritten Character Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
FGCN '07 Proceedings of the Future Generation Communication and Networking - Volume 02
Modelling of Behavioural Patterns for Abnormality Detection in the Context of Lifestyle Reassurance
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Abnormal human behavioral pattern detection in assisted living environments
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Use cases for abnormal behaviour detection in smart homes
ICOST'10 Proceedings of the Aging friendly technology for health and independence, and 8th international conference on Smart homes and health telematics
Abnormality detection for improving elder's daily life independent
ICOST'10 Proceedings of the Aging friendly technology for health and independence, and 8th international conference on Smart homes and health telematics
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Modeling individual healthy behavior using home automation sensor data: Results from a field trial
Journal of Ambient Intelligence and Smart Environments - Design and Deployment of Intelligent Environments
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Identifying abnormal behaviour is an important factor in activity recognition. The aim of this paper is to design a system able to detect the abnormal behaviours of daily activity living in an intelligent environment. We approach this by applying dissimilarity (distance) measures on data collected from a single inhabitant environment. The data are acquired from occupancy sensors such as a door and motion sensors. Since the data is collected from these sensors has a discrete value either on or off, only the binary dissimilarity measures are considered in this paper. There are several distance measurements which find the mismatching bits of two binary data sets. In this paper, two major dissimilarity measures, which include hamming distance and fuzzy hamming distance, are used and compared. These measures can help in distinguishing between normal and abnormal behaviour patterns in order to improve the quality of elderly people's lives. Two case studies where the inhabitants suffer from dementia are used to verify the accuracy of the results. The experimental results demonstrate that fuzzy hamming distance gives a smaller distance than classic hamming distance in the case of motion sensors over door sensors.