14,376 research outputs found
Aggregating multiple body sensors for analysis in sports
Real time monitoring of the wellness of sportspersons, during their sporting activity and training, is important in order to maximise performance during the sporting event itself and during training, as well as being important for the health of the sportsperson overall. We have combined a suite of common, off-the-shelf sensors with specialist body sensing technology we are developing ourselves and constructed a software system for recording, analysing and presenting sensed data gathered from a single player during a sporting activity, a football match. We gather readings for heart rate, galvanic skin response, motion, heat flux, respiration, and location (GPS) using on-body sensors, while simultaneously tracking player activity using a combination of a playercam video and pitch-wide video recording. We have aggregated all this sensed data into a single overview of player performance and activity which can be reviewed, post-event. We are currently working on integrating other non-invasive methods for real-time on-body monitoring of sweat electrolytes and pH via a textile-based sweat sampling and analysis platform. Our work is heading in two directions; firstly from post-event data aggregation to real-time monitoring, and secondly, to convert raw sensor readings into performance indicators that are meaningful to practitioners in the field
Integrating multiple sensor modalities for environmental monitoring of marine locations
In this paper we present preliminary work on integrating
visual sensing with the more traditional sensing modalities
for marine locations. We have deployed visual sensing at one
of the Smart Coast WSN sites in Ireland and have built a
software platform for gathering and synchronizing all sensed
data. We describe how the analysis of a range of different
sensor modalities can reinforce readings from a given noisy,
unreliable sensor
The role of linked data and the semantic web in building operation
Effective Decision Support Systems (DSS) for building service managers require adequate performance data from many building data silos in order to deliver a complete view of building performance. Current performance analysis techniques tend to focus on a limited number of data sources, such as BMS measured data (temperature, humidity, C02), excluding a wealth of other data sources increasingly available in the modern building, including weather data, occupant feedback, mobile sensors & feedback systems, schedule information, equipment usage information. This paper investigates the potential for using Linked Data and Semantic Web technologies to improve interoperability across AEC domains, overcoming many of the roadblocks hindering information transfer currently
Giving neurons to sensors. QoS management in wireless sensors networks
Public utilities services (gas, water and electricity)
have been traditionally automated with several technologies. The
main functions that these technologies must support are AMR,
Automated Meter Reading, and SCADA, Supervisory Control
And Data Acquisition. Most meter manufacturers provide devices
with Bluetoothr or ZigBeeTM communication features. This characteristic
has allowed the inclusion of wireless sensor networks
(WSN) in these systems. Once WSNs have appeared in such
a scenario, real-time AMR and SCADA applications can be
developed with low cost. Data must be routed from every meter to
a base station. This paper describes the use of a novel QoS-driven
routing algorithm, named SIR: Sensor Intelligence Routing, over
a network of meters. An arti cial neural network is introduced
in every node to manage the routes that data have to follow. The
resulting system is named Intelligent Wireless Sensor Network
(IWSN)
Using Artificial Intelligence in Wireless Sensor Routing Protocols
This paper represents a dissertation about how an artificial
intelligence technique can be applied to wireless sensor networks. Due
to the constraints on data processing and power consumption, the use
of artificial intelligence has been historically discarded in these kind of
networks. However, in some special scenarios the features of neural networks
are appropriate to develop complex tasks such as path discovery.
In this paper, we explore the performance of two very well known routing
paradigms, directed diffusion and Energy-Aware Routing, and our
routing algorithm, named SIR, which has the novelty of being based
on the introduction of neural networks in every sensor node. Extensive
simulations over our wireless sensor network simulator, OLIMPO, have
been carried out to study the efficiency of the introduction of neural networks.
A comparison of the results obtained with every routing protocol
is analyzed
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