18,577 research outputs found
Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation
We present results from a set of experiments in this pilot study to
investigate the causal influence of user activity on various environmental
parameters monitored by occupant carried multi-purpose sensors. Hypotheses with
respect to each type of measurements are verified, including temperature,
humidity, and light level collected during eight typical activities: sitting in
lab / cubicle, indoor walking / running, resting after physical activity,
climbing stairs, taking elevators, and outdoor walking. Our main contribution
is the development of features for activity and location recognition based on
environmental measurements, which exploit location- and activity-specific
characteristics and capture the trends resulted from the underlying
physiological process. The features are statistically shown to have good
separability and are also information-rich. Fusing environmental sensing
together with acceleration is shown to achieve classification accuracy as high
as 99.13%. For building applications, this study motivates a sensor fusion
paradigm for learning individualized activity, location, and environmental
preferences for energy management and user comfort.Comment: submitted to the 40th Annual Conference of the IEEE Industrial
Electronics Society (IECON
PresenceSense: Zero-training Algorithm for Individual Presence Detection based on Power Monitoring
Non-intrusive presence detection of individuals in commercial buildings is
much easier to implement than intrusive methods such as passive infrared,
acoustic sensors, and camera. Individual power consumption, while providing
useful feedback and motivation for energy saving, can be used as a valuable
source for presence detection. We conduct pilot experiments in an office
setting to collect individual presence data by ultrasonic sensors, acceleration
sensors, and WiFi access points, in addition to the individual power monitoring
data. PresenceSense (PS), a semi-supervised learning algorithm based on power
measurement that trains itself with only unlabeled data, is proposed, analyzed
and evaluated in the study. Without any labeling efforts, which are usually
tedious and time consuming, PresenceSense outperforms popular models whose
parameters are optimized over a large training set. The results are interpreted
and potential applications of PresenceSense on other data sources are
discussed. The significance of this study attaches to space security, occupancy
behavior modeling, and energy saving of plug loads.Comment: BuildSys 201
GREEND: An Energy Consumption Dataset of Households in Italy and Austria
Home energy management systems can be used to monitor and optimize
consumption and local production from renewable energy. To assess solutions
before their deployment, researchers and designers of those systems demand for
energy consumption datasets. In this paper, we present the GREEND dataset,
containing detailed power usage information obtained through a measurement
campaign in households in Austria and Italy. We provide a description of
consumption scenarios and discuss design choices for the sensing
infrastructure. Finally, we benchmark the dataset with state-of-the-art
techniques in load disaggregation, occupancy detection and appliance usage
mining
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