Ambient-aware continuous care through semantic context dissemination

Ongenae, Femke; Famaey, Jeroen; Verstichel, Stijn; Zutter, Saar De; Latré, Steven; Ackaert, Ann; Verhoeve, Piet; Turck, Filip De

doi:10.1186/1472-6947-14-97

Cited by 6 publications

(7 citation statements)

References 52 publications

(58 reference statements)

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“…In the measurement we did not include the time needed for this registration as this heavily depends on the complexity of the filter rule, the amount of filter rules already present in the SCB and the size of the ontology used by the SCB. This was already thoroughly evaluated in previous research [31], which has shown that in most realistic scenarios this time is under 1 second.…”

Section: Evaluation Of Quality Attribute Scenariosmentioning

confidence: 98%

Semantics-based platform for context-aware and personalized robot interaction in the internet of robotic things

Mahieu

Ongenae

Backere

et al. 2019

Journal of Systems and Software

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Robots are moving from well-controlled lab environments to the real world, where an increasing number of environments has been transformed into smart sensorized IoT spaces. Users will expect these robots to adapt to their preferences and needs, and even more so for social robots that engage in personal interactions. In this paper, we present declarative ontological models and a middleware platform for building services that generate interaction tasks for social robots in smart IoT environments. The platform implements a modular, data-driven workflow that allows developers of interaction services to determine the appropriate time, content and style of human-robot interaction tasks by reasoning on semantically enriched IoT sensor data. The platform also abstracts the complexities of scheduling, planning and execution of these tasks, and can automatically adjust parameters to the personal profile and current context. We present motivational scenarios in three environments: a smart home, a smart office and a smart nursing home, detail the interfaces and executional paths in our platform and present a proof-of-concept implementation.

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Section: Evaluation Of Quality Attribute Scenariosmentioning

confidence: 98%

Semantics-based platform for context-aware and personalized robot interaction in the internet of robotic things

Mahieu

Ongenae

Backere

et al. 2019

Journal of Systems and Software

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“…There is one LRS per room, independent of the amount of patients. Over the full hospital, only one BRS exists 4 .…”

Section: Architectural Use Case Set-upmentioning

confidence: 99%

“…Moreover, the ontology contains some concepts for this use case that allow the inference of certain situations and hence easier query writing. An example is NursePresentObservation, which is defined as a BLE tag observation of a bracelet owned by a nurse: 4 For this implementation, a simple set-up with only one BRS for the full hospital is considered. However, in a real-life set-up, there will typically be more than one BRS component in the system, e.g., an extra BRS per department, as indicated on The existing ACCIO ontology has been further extended for this work with some key concepts and relations specific for the current use case.…”

Section: Continuous Care Ontologymentioning

confidence: 99%

“…The advantage of the IoT is that the data streams originating from the various sensors and devices can be combined with this knowledge to derive new knowledge about the environment and the patient's current condition [3]. This enables devices to achieve situation-and context-awareness, and enables better support of the nursing staff in their activities [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Towards a Cascading Reasoning Framework to Support Responsive Ambient-Intelligent Healthcare Interventions

Brouwer¹,

Ongenae²,

Bonte³

et al. 2018

Preprint

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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 seconds, allowing for responsive intervention by a nurse in alarming situations. Using the evaluation results, the benefits of cascading reasoning for healthcare are analyzed.

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“…It does not matter if some information, which is sent to the OCarePlatform, is not relevant. By using the Semantic Communication Bus (SCB) [35], the relevant information is sent to the services, which have indicated a specific interest in that type of data. As often false positives and negative are generated from single sensors or fall detection systems, the OCarePlatform is used to combine all this information and come to 1 conclusion.…”

Section: The Ocareplatformmentioning

confidence: 99%

Social-aware Event Handling within the FallRisk Project

Backere¹,

Bergh²,

Coppers³

et al. 2017

Methods Inf Med

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SummaryObjectives: With the uprise of the Internet of Things, wearables and smartphones are moving to the foreground. Ambient Assisted Living solutions are, for example, created to facilitate ageing in place. One example of such systems are fall detection systems. Currently, there exists a wide variety of fall detection systems using different methodologies and technologies. However, these systems often do not take into account the fall handling process, which starts after a fall is identified or this process only consists of sending a notification. The FallRisk system delivers an accurate analysis of incidents occurring in the home of the older adults using several sensors and smart devices. Moreover, the input from these devices can be used to create a socialaware event handling process, which leads to assisting the older adult as soon as possible and in the best possible way. Methods: The FallRisk system consists of several components, located in different places. When an incident is identified by the FallRisk system, the event handling process will be followed to assess the fall incident and select the most appropriate caregiver, based on the input of the smartphones of the caregivers. In this process, availability and location are automatically taken into account. Results: The event handling process was evaluated during a decision tree workshop to verify if the current day practices reflect the requirements of all the stakeholders. Other knowledge, which is uncovered during this workshop can be taken into account to further improve the process. Conclusions: The FallRisk offers a way to detect fall incidents in an accurate way and uses context information to assign the incident to the most appropriate caregiver. This way, the consequences of the fall are minimized and help is at location as fast as possible. It could be concluded that the current guidelines on fall handling reflect the needs of the stakeholders. However, current technology evolutions, such as the uptake of wearables and smartphones, enables the improvement of these guidelines, such as the automatic ordering of the caregivers based on their location and availability.

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Ambient-aware continuous care through semantic context dissemination

Cited by 6 publications

References 52 publications

Semantics-based platform for context-aware and personalized robot interaction in the internet of robotic things

Semantics-based platform for context-aware and personalized robot interaction in the internet of robotic things

Towards a Cascading Reasoning Framework to Support Responsive Ambient-Intelligent Healthcare Interventions

Social-aware Event Handling within the FallRisk Project

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