SensorCentral: A Research Oriented, Device Agnostic, Sensor Data Platform

Rafferty, Joseph; Synnott, Jonathan; Ennis, Andrew; Nugent, Chris; McChesney, Ian; Cleland, Ian

doi:10.1007/978-3-319-67585-5_11

Cited by 21 publications

(21 citation statements)

References 18 publications

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“…For this work we only made use of the TISs. The two TISs were set up as sources for the SensorCentral sensor data platform [15]. The sensor data were provided by the SensorCentral sensor data platform in JSON format.…”

Section: Methodsmentioning

confidence: 99%

Fusing Thermopile Infrared Sensor Data for Single Component Activity Recognition within a Smart Environment

Burns

Morrow

Nugent

et al. 2019

JSAN

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To provide accurate activity recognition within a smart environment, visible spectrum cameras can be used as data capture devices in solution applications. Privacy, however, is a significant concern with regards to monitoring in a smart environment, particularly with visible spectrum cameras. Their use, therefore, may not be ideal. The need for accurate activity recognition is still required and so an unobtrusive approach is addressed in this research highlighting the use of a thermopile infrared sensor as the sole means of data collection. Image frames of the monitored scene are acquired from a thermopile infrared sensor that only highlights sources of heat, for example, a person. The recorded frames feature no discernable characteristics of people; hence privacy concerns can successfully be alleviated. To demonstrate how thermopile infrared sensors can be used for this task, an experiment was conducted to capture almost 600 thermal frames of a person performing four single component activities. The person’s position within a room, along with the action being performed, is used to appropriately predict the activity. The results demonstrated that high accuracy levels, 91.47%, for activity recognition can be obtained using only thermopile infrared sensors.

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Section: Methodsmentioning

confidence: 99%

Fusing Thermopile Infrared Sensor Data for Single Component Activity Recognition within a Smart Environment

Burns

Morrow

Nugent

et al. 2019

JSAN

Self Cite

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“…The image data is collected from the TVS by means of a twisted ethernet cable which is connected to the local area network. The middleware SensorCentral [15] integrates the TVS as a sensor source, providing the data within a Web Service in JSON format.…”

Section: Agile Data Collection From Thermal Vision Sensormentioning

confidence: 99%

Detection of Falls from Non-Invasive Thermal Vision Sensors Using Convolutional Neural Networks

et al. 2018

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In this work, we detail a methodology based on Convolutional Neural Networks (CNNs) to detect falls from non-invasive thermal vision sensors. First, we include an agile data collection to label images in order to create a dataset that describes several cases of single and multiple occupancy. These cases include standing inhabitants and target situations with a fallen inhabitant. Second, we provide data augmentation techniques to increase the learning capabilities of the classification and reduce the configuration time. Third, we have defined 3 types of CNN to evaluate the impact that the number of layers and kernel size have on the performance of the methodology. The results show an encouraging performance in single-occupancy contexts, with up to 92 % of accuracy, but a 10 % of reduction in accuracy in multiple-occupancy. The learning capabilities of CNNs have been highlighted due to the complex images obtained from the low-cost device. These images have strong noise as well as uncertain and blurred areas. The results highlight that the CNN based on 3-layers maintains a stable performance, as well as quick learning.

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“…Dataset formatting: This component connects with the JSON query objects submitted from the user interface tier to populate, edit or query the data stored in the ontology database. It can also receive datasets exported in JSON or XES format from SensorCentral [9]. This will be achieved through SPARQL updates against a Virtuoso database.…”

Section: Odi Portal-process Logic and Data Tiersmentioning

confidence: 99%

Portal Design for the Open Data Initiative: A Preliminary Study

et al. 2018

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The Open Data Initiative (ODI) has been previously proposed to facilitate the sharing of annotated datasets within the pervasive health care research community. This paper outlines the requirements for the ODI portal based on the ontological data model of the ODI and its typical usage scenarios. In the context of an action research framework, the paper outlines the ODI platform, the design of a prototype user interface for the purposes of initial evaluation and its technical review by third-party researchers (n = 3). The main findings from the technical review were found to be the need for a more flexible user interface to reflect the different experimental configurations in the research community, provision for describing dataset usage, and dissemination conditions. The technical review also identified the value of permitting datasets with variable quality, as noisy datasets are useful in the testing of activity recognition algorithms. Revisions to the ODI ontology and platform are proposed based on the findings from this study.

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SensorCentral: A Research Oriented, Device Agnostic, Sensor Data Platform

Cited by 21 publications

References 18 publications

Fusing Thermopile Infrared Sensor Data for Single Component Activity Recognition within a Smart Environment

Fusing Thermopile Infrared Sensor Data for Single Component Activity Recognition within a Smart Environment

Detection of Falls from Non-Invasive Thermal Vision Sensors Using Convolutional Neural Networks

Portal Design for the Open Data Initiative: A Preliminary Study

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