On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Learning Significant Locations and Predicting User Movement with GPS
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Online Amnesic Approximation of Streaming Time Series
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Motion-Alert: automatic anomaly detection in massive moving objects
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Improving the classification accuracy of streaming data using SAX similarity features
Pattern Recognition Letters
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In this paper we illustrate the potential of motion behaviour analysis in assessing the wellbeing of unsupervised, vulnerable individuals. By learning the routine motion behaviour of the subject (i.e. places visited, routes taken between places) we show it is possible to detect unusual behaviours while they are happening. This requires the processing of continuous sensor data streams, and real-time recognition of the subject's behaviour. To address privacy concerns, analysis will be performed locally to the subject on a small computing device. Current data mining techniques were not developed for restricted computing environments, nor for the demands of real-time behaviour recognition. In this paper we present a novel, online technique for discretizing a sensor data stream that supports both unsupervised learning of human motion behaviours and real-time recognition. We performed experiments using GPS data and compared the results of Dynamic Time Warping.