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Towards a cascading reasoning framework to support responsive ambient-intelligent healthcare interventions

Mathias De Brouwer (UGent) , Femke Ongenae (UGent) , Pieter Bonte (UGent) and Filip De Turck (UGent)
(2018) SENSORS. 18(10).
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Abstract
In hospitals and smart nursing homes, ambient-intelligent care rooms are equipped with many sensors. They can monitor environmental and body parameters, and detect wearable devices of patients and nurses. Hence, they continuously produce data streams. This offers the opportunity to collect, integrate and interpret this data in a context-aware manner, with a focus on reactivity and autonomy. However, doing this in real time on huge data streams is a challenging task. In this context, cascading reasoning is an emerging research approach that exploits the trade-off between reasoning complexity and data velocity by constructing a processing hierarchy of reasoners. Therefore, a cascading reasoning framework is proposed in this paper. A generic architecture is presented allowing to create a pipeline of reasoning components hosted locally, in the edge of the network, and in the cloud. The architecture is implemented on a pervasive health use case, where medically diagnosed patients are constantly monitored, and alarming situations can be detected and reacted upon in a context-aware manner. A performance evaluation shows that the total system latency is mostly lower than 5 s, allowing for responsive intervention by a nurse in alarming situations. Using the evaluation results, the benefits of cascading reasoning for healthcare are analyzed.
Keywords
ONTOLOGY, MANAGEMENT, STREAMS, SPARQL, pervasive healthcare, cascading reasoning, stream reasoning

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Please use this url to cite or link to this publication:

Chicago
De Brouwer, Mathias, Femke Ongenae, Pieter Bonte, and Filip De Turck. 2018. “Towards a Cascading Reasoning Framework to Support Responsive Ambient-intelligent Healthcare Interventions.” Sensors 18 (10).
APA
De Brouwer, Mathias, Ongenae, F., Bonte, P., & De Turck, F. (2018). Towards a cascading reasoning framework to support responsive ambient-intelligent healthcare interventions. SENSORS, 18(10).
Vancouver
1.
De Brouwer M, Ongenae F, Bonte P, De Turck F. Towards a cascading reasoning framework to support responsive ambient-intelligent healthcare interventions. SENSORS. 2018;18(10).
MLA
De Brouwer, Mathias, Femke Ongenae, Pieter Bonte, et al. “Towards a Cascading Reasoning Framework to Support Responsive Ambient-intelligent Healthcare Interventions.” SENSORS 18.10 (2018): n. pag. Print.
@article{8581923,
  abstract     = {In hospitals and smart nursing homes, ambient-intelligent care rooms are equipped with many sensors. They can monitor environmental and body parameters, and detect wearable devices of patients and nurses. Hence, they continuously produce data streams. This offers the opportunity to collect, integrate and interpret this data in a context-aware manner, with a focus on reactivity and autonomy. However, doing this in real time on huge data streams is a challenging task. In this context, cascading reasoning is an emerging research approach that exploits the trade-off between reasoning complexity and data velocity by constructing a processing hierarchy of reasoners. Therefore, a cascading reasoning framework is proposed in this paper. A generic architecture is presented allowing to create a pipeline of reasoning components hosted locally, in the edge of the network, and in the cloud. The architecture is implemented on a pervasive health use case, where medically diagnosed patients are constantly monitored, and alarming situations can be detected and reacted upon in a context-aware manner. A performance evaluation shows that the total system latency is mostly lower than 5 s, allowing for responsive intervention by a nurse in alarming situations. Using the evaluation results, the benefits of cascading reasoning for healthcare are analyzed.},
  articleno    = {3514},
  author       = {De Brouwer, Mathias and Ongenae, Femke and Bonte, Pieter and De Turck, Filip},
  issn         = {1424-8220},
  journal      = {SENSORS},
  keywords     = {ONTOLOGY,MANAGEMENT,STREAMS,SPARQL,pervasive healthcare,cascading reasoning,stream reasoning},
  language     = {eng},
  number       = {10},
  pages        = {32},
  title        = {Towards a cascading reasoning framework to support responsive ambient-intelligent healthcare interventions},
  url          = {http://dx.doi.org/10.3390/s18103514},
  volume       = {18},
  year         = {2018},
}

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