For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. The proposed approach allows for mixed time-series -- containing both pattern and non-pattern data -- such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. We present the early results of our approach in the context of monitoring the health status of a person at home. The purpose is to build a behavioral profile of a person by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors installed in the home.
Most of the current works dealing with home health telecare are focused either on implementing a generic architecture for the integrated medical information system, on improving the daily life of patients using various automatic devices, specific equipment, and basic alarms, or on providing health care services to patients suffering from specific diseases like asthma, diabetes, cardiac, pulmonary, or Alzheimers. Rialle have presented in @cite_9 an overview of projects related to home health telecare. Basic alarms are raised by smart sensors or low layers of a local intelligence unit when a problem occurs at a short temporal scale: either one parameter overpasses a critical value (nocturia, pollakisuria, fall, hypertensive crisis, etc.), or a critical scenario involving the value of possibly more than one parameter is recognized (asthma crisis, etc.). Our focus is on the broadcasting of high level alarms about the persons health status, which concern a larger temporal scale. That issue is solved by first learning the daily living habits of a person to be able to detect later unusual situations. That behavioral profile is built by mining heterogeneous multivariate temporal data collected from sensors installed at home for learning meaningful patterns.
@cite_9
2728787983
New healthcare technologies are emerging with the increasing age of the society, where the development of smart homes for monitoring the elders’ activities is in the center of them. Identifying the resident’s activities in an apartment is an important module in such systems. Dense sensing approach aims to embed sensors in the environment to report the detected events continuously. The events are segmented and analyzed via classifiers to identify the corresponding activity. Although several methods were introduced in recent years for detecting simple activities, the recognition of complex ones requires more effort. Due to the different time duration and event density of each activity, finding the best size of the segments is one of the challenges in detecting the activity. Also, using appropriate classifiers that are capable of detecting simple and interleaved activities is the other issue. In this paper, we devised a two-phase approach called CARER (Complex Activity Recognition using Emerging patterns and Random forest). In the first phase, the emerging patterns are mined, and various features of the activities are extracted to build a model using the Random Forest technique. In the second phase, the sequences of events are segmented dynamically by considering their recency and sensor correlation. Then, the segments are analyzed by the generated model from the previous phase to recognize both simple and complex activities. We examined the performance of the devised approach using the CASAS dataset. To do this, first we investigated several classifiers. The outcome showed that the combination of emerging patterns and the random forest provide a higher degree of accuracy. Then, we compared CARER with the static window approach, which used Hidden Markov Model. To have a fair comparison, we replaced the dynamic segmentation module of CARER with the static one. The results showed more than 12 improvement in f-measure. Finally, we compared our work with Dynamic sensor segmentation for real-time activity recognition, which used dynamic segmentation. The f-measure metric demonstrated up to 12.73 improvement.