9,802 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
Activities recognition and worker profiling in the intelligent office environment using a fuzzy finite state machine
Analysis of the office workers’ activities of daily working in an intelligent office environment can be used to optimize energy consumption and also office workers’ comfort. To achieve this end, it is essential to recognise office workers’ activities including short breaks, meetings and non-computer activities to allow an optimum control strategy to be implemented. In this paper, fuzzy finite state machines are used to model an office worker’s behaviour. The model will incorporate sensory data collected from the environment as the input and some pre-defined fuzzy states are used to develop the model. Experimental results are presented to illustrate the effectiveness of this approach. The activity models of different individual workers as inferred from the sensory devices can be distinguished. However, further investigation is required to create a more complete model
Sensor-based early activity recognition inside buildings to support energy and comfort management systems
Building Energy and Comfort Management (BECM) systems have the potential to considerably reduce costs related to energy consumption and improve the efficiency of resource exploitation, by implementing strategies for resource management and control and policies for Demand-Side Management (DSM). One of the main requirements for such systems is to be able to adapt their management decisions to the users’ specific habits and preferences, even when they change over time. This feature is fundamental to prevent users’ disaffection and the gradual abandonment of the system. In this paper, a sensor-based system for analysis of user habits and early detection and prediction of user activities is presented. To improve the resulting accuracy, the system incorporates statistics related to other relevant external conditions that have been observed to be correlated (e.g., time of the day). Performance evaluation on a real use case proves that the proposed system enables early recognition of activities after only 10 sensor events with an accuracy of 81%. Furthermore, the correlation between activities can be used to predict the next activity with an accuracy of about 60%
Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review
Animals play a profoundly important and intricate role in our lives today.
Dogs have been human companions for thousands of years, but they now work
closely with us to assist the disabled, and in combat and search and rescue
situations. Farm animals are a critical part of the global food supply chain,
and there is increasing consumer interest in organically fed and humanely
raised livestock, and how it impacts our health and environmental footprint.
Wild animals are threatened with extinction by human induced factors, and
shrinking and compromised habitat. This review sets the goal to systematically
survey the existing literature in smart computing and sensing technologies for
domestic, farm and wild animal welfare. We use the notion of \emph{animal
welfare} in broad terms, to review the technologies for assessing whether
animals are healthy, free of pain and suffering, and also positively stimulated
in their environment. Also the notion of \emph{smart computing and sensing} is
used in broad terms, to refer to computing and sensing systems that are not
isolated but interconnected with communication networks, and capable of remote
data collection, processing, exchange and analysis. We review smart
technologies for domestic animals, indoor and outdoor animal farming, as well
as animals in the wild and zoos. The findings of this review are expected to
motivate future research and contribute to data, information and communication
management as well as policy for animal welfare
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