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 2176664831 1981094339 2104956067
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. With gigabit networking becoming economically feasible and widely installed at homes, there are new opportunities to revisit in-home, personalized telehealth services. In this paper, we describe a novel telehealth eldercare service that we developed viz., "PhysicalTherapy-as-a-Service" (PTaaS) that connects a remote physical therapist at a clinic to a senior at home. The service leverages a high-speed, low-latency network connection through an interactive interface built on top of Microsoft Kinect motion sensing capabilities. The interface that is built using user-centered design principles for wellness coaching exercises is essentially a 'Synchronous Big Data' application due to its: (i) high data-in-motion velocity (i.e., peak data rate is ? 400 Mbps), (ii) considerable variety (i.e., measurements include 3D sensing, network health, user opinion surveys and video clips of RGB, skelet al and depth data), and (iii) large volume (i.e., several GB of measurement data for a simple exercise activity). The successful PTaaS delivery through this interface is dependent on the veracity analytics needed for correlation of the real-time Big Data streams within a session, in order to assess exercise balance of the senior without any bias due to network quality effects. Our experiments with PTaaS in an actual testbed involving senior homes in Kansas City with Google Fiber connections and our university clinic demonstrate the network configuration and time synchronization related challenges in order to perform online analytics. Our findings provide insights on how to: (a) enable suitable resource calibration and perform network troubleshooting for high user experience for both the therapist and the senior, and (b) realize a Big Data architecture for PTaaS and other similar personalized healthcare services to be remotely delivered at a large-scale in a reliable, secure and cost-effective manner. The size of multi-modal, heterogeneous data collected through various sensors is growing exponentially. It demands intelligent data reduction, data mining and analytics at edge devices. Data compression can reduce the network bandwidth and transmission power consumed by edge devices. This paper proposes, validates and evaluates Fog Data, a service-oriented architecture for Fog computing. The center piece of the proposed architecture is a low power embedded computer that carries out data mining and data analytics on raw data collected from various wearable sensors used for telehealth applications. The embedded computer collects the sensed data as time series, analyzes it, and finds similar patterns present. Patterns are stored, and unique patterns are transmited. Also, the embedded computer extracts clinically relevant information that is sent to the cloud. A working prototype of the proposed architecture was built and used to carry out case studies on telehealth big data applications. Specifically, our case studies used the data from the sensors worn by patients with either speech motor disorders or cardiovascular problems. We implemented and evaluated both generic and application specific data mining techniques to show orders of magnitude data reduction and hence transmission power savings. Quantitative evaluations were conducted for comparing various data mining techniques and standard data compression techniques. The obtained results showed substantial improvement in system efficiency using the Fog Data architecture. By 2050, about one third of the French population will be over 65. Our laboratory's current research focuses on the monitoring of elderly people at home, to detect a loss of autonomy as early as possible. Our aim is to quantify criteria such as the international activities of daily living (ADL) or the French Autonomie Gerontologie Groupes Iso-Ressources (AGGIR) scales, by automatically classifying the different ADL performed by the subject during the day. A Health Smart Home is used for this. Our Health Smart Home includes, in a real flat, infrared presence sensors (location), door contacts (to control the use of some facilities), temperature and hygrometry sensor in the bathroom, and microphones (sound classification and speech recognition). A wearable kinematic sensor also informs postural transitions (using pattern recognition) and walk periods (frequency analysis). This data collected from the various sensors are then used to classify each temporal frame into one of the ADL that was previously acquired (seven activities: hygiene, toilet use, eating, resting, sleeping, communication, and dressing undressing). This is done using support vector machines. We performed a 1-h experimentation with 13 young and healthy subjects to determine the models of the different activities, and then we tested the classification algorithm (cross validation) with real data.