A sensor system capable of automatically recognizing activities would allow many potential ubiquitous applications. In this paper, we present an easy to install sensor network and an accurate but inexpensive annotation method. A recorded dataset consisting of 28 days of sensor data and its annotation is described and made available to the community. Through a number of experiments we show how the hidden Markov model and conditional random fields perform in recognizing activities. We achieve a timeslice accuracy of 95.6% and a class accuracy of 79.4%.

The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were acquired by the NSF-funded Center for Airborne Laser Mapping over the University of Houston campus and its neighboring area in the summer of 2012. This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the fusion of spectral, spatial, and elevation information.

In this paper we present a system that is able to monitor activities of people in a domestic environment. The application area is that of ambient assisted living. Because of the increasing number of elderly living at home and the foreseen shortage in the number of caregivers, remote ICTbased applications will have to be developed. One of the tasks of such systems is to monitor elderly in their ability to perform the activities of daily living (ADL). We developed a system consisting of a low-power, low bandwidth wireless sensor network, incorporating simple switch sensors. As a pilot the system was installed in the apartment of an elderly lady. The data were partly annotated by the woman self. We developed a pattern recognition method based on probabilistic models. The detected clusters compare well with the labels of the annotation. I. INTRODUCTION he growing population of elders in our society calls for a new approach in care giving. To avoid rising costs associated with hospitalization, nursing homes or day care centers, it is important that people continue to live in their homes as long as possible. Living independently also contributes to a feeling of well-being. In order for elders to extend their lives at home, some form of automatic health monitoring is required. A good indicator of the health status of elderly, particularly their cognitive status, is the ability to perform activities of daily living (ADL). Examples of such ADL's are bathing, cleaning, and cooking [1]. Usually questionnaires are used to assess the performance of the elder. However, such a method is costly and an assessment of the health status can not be done frequently and completely. By doing this with a telemonitoring system the work can be transferred from the home care professionals to a remote professional. An automated monitoring system would give much more insight and would ease the step from living independently to assisted living. A monitoring system will also be of use to the family and home care workers. For example, if it can be detected that in the morning somebody got out of bed and dressed, the home care worker can visit the elderly at home. This makes the process much more efficient. Telemonitoring is still not common because of a number Manuscript received 30 April 2008..

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F e A t u R e : A c t I v I t y R e c o g n I t I o n 68 PER VA SI V E computing www.computer.org/pervasive FeAtuRe: ActIvIty RecognItIon 6. D.