Sensor data fusion for activity monitoring in the PERSONA ambient assisted living project

Amoretti, Michele; Copelli, Sergio; Wientapper, Folker; Furfari, Francesco; Lenzi, Stefano; Chessa, Stefano

doi:10.1007/s12652-011-0095-6

Cited by 46 publications

(24 citation statements)

References 37 publications

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“…However, as ontologies often are centralized [18,19,20,21,22,23], the system may be robbed of its scalability and performance. In this paper, we presented the OCarePlatform, a semantic modular back-end system.…”

Section: Discussionmentioning

confidence: 99%

“…By using a communication bus with core ontologies, responsible for forwarding the data to specific services, the SCB performs efficient and is no bottleneck for the system, unlike the systems discussed in [18,19,20,21,22,23].…”

Section: Discussionmentioning

confidence: 99%

“…Context-aware applications can benefit from incorporating ontologies as these models enable knowledge sharing, reasoning and increase interoperability [17]. Currently, semantic knowledge components in AAL systems [18,19,20,21,22,23] are always deployed as a central entity within the framework. Such an approach negatively impacts the scalability and performance of the entire system.…”

Section: Introductionmentioning

confidence: 99%

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The OCarePlatform: A context-aware system to support independent living

Backere

Bonte

Verstichel

et al. 2017

Computer Methods and Programs in Biomedicine

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Background: Currently, healthcare services, such as institutional care facilities, are burdened with an increasing number of elderly people and individuals with chronic illnesses and a decreasing number of competent caregivers.Objectives: To relieve the burden on healthcare services, independent living at home could be facilitated, by offering individuals and their (in)formal caregivers support in their daily care and needs. With the rise of pervasive healthcare, new information technology solutions can assist elderly people ("residents") and their caregivers to allow residents to live independently for as long as possible.Methods: To this end, the OCarePlatform system was designed. This semantic, data-driven and cloud-based back-end system facilitates independent living by offering information and knowledge-based services to the resident and his/her (in)formal caregivers. Data and context information are gathered to realize context-aware and personalized services and to support residents in meeting their daily needs. This body of data, originating from heterogeneous data and information sources, is sent to personalized services, where is fused, thus creating an overview of the resident's current situation.Results: The architecture of the OCarePlatform is proposed, which is based on a service-oriented approach, together with its different components and their interactions. The implementation details are presented, together with a run- * Corresponding author; Tel: +32 9 331 49 38; Fax: +32 9 331 48 99Email addresses: femke.debackere@intec.ugent.be (F. De Backere), pieter.bonte@intec.ugent.be (P. Bonte), stijn.verstichel@intec.ugent.be (S. Verstichel), femke.ongenae@intec.ugent.be (F. Ongenae), filip.deturck@intec.ugent.be (F. De Turck) Preprint submitted to ElsevierNovember 25, 2016 ning example. A scalability and performance study of the OCarePlatform was performed. The results indicate that the OCarePlatform is able to support a realistic working environment and respond to a trigger in less than 5 seconds.The system is highly dependent on the allocated memory.Conclusions: The data-driven character of the OCarePlatform facilitates easy plug-in of new functionality, enabling the design of personalized, context-aware services. The OCarePlatform leads to better support for elderly people and individuals with chronic illnesses, who live independently.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The OCarePlatform: A context-aware system to support independent living

Backere

Bonte

Verstichel

et al. 2017

Computer Methods and Programs in Biomedicine

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“…An image segmentation algorithm based on improved... Bayesian decision (Amoretti et al 2013;Choi and Baraniuk 2001) uses the similarity of different features of the image and the model of known parameters to label different regions, and obtain the segmentation results at different scales. With this theorem, combined with Eqs.…”

Section: Improved Multiscale Random Field Model In Wavelet Domainmentioning

confidence: 99%

An image segmentation algorithm based on improved multiscale random field model in wavelet domain

Tang

Wang

2015

J Ambient Intell Human Comput

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Image segmentation is the key step in image analysis and image manipulating. Image segmentation based on multiscale random field model in wavelet domain (WMSRF) is a useful implementation tool. It can capture image structure information in different resolution and reduce the reliance on initial segmentation. However, WMSRF has boundary block effect and its operating efficiency is low. In this paper we propose an improved segmentation algorithm based on WMSRF (improved WMSRF). The improved WMSRF algorithm consists of two fields: the image characteristic field and the labeling field. The former is built on a series of boundary that is extracted by wavelet transform, and modeled by Gauss-MRF. The latter is also built on the boundary in corresponding scale, and modeled by multiscale random field (MSRF). Both fields constrain each other at the joint probability. This integrates interactions in inter-scale and inner-scale, and helps to describe image's non-stationary property. Then the parameters in the models are estimated by using expectation-maximization. Consequently the segmentation result of initial image is achieved by using Bayesian and sequential maximum a posteriori estimation. In this paper, the medical images are utilized as experiment images. The simulations are compared with the WMSRF algorithm and the results show the improved algorithm can not only distinguish different regions effectively, but also improve the efficiency.

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“…Designing assisted healthcare systems for dementia care, by monitoring the activities of the occupant in a smart environment, are a challenge as it requires personalized ubiquitous computing [1][2][3]. In the context of dementia care, abnormal activities are those that arise due to memory loss.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical activity recognition for dementia care using Markov Logic Network

Gayathri

Elias

Ravindran

2014

Pers Ubiquit Comput

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Statistics reveal that globally, the aging population in different stages of dementia are struggling to cope with daily activities and are progressively becoming dependent on care takers thereby making dementia care a challenging social problem. Healthcare systems in smart environments that aim to address this growing social need require the support of technology to recognize and respond in an ubiquitous manner. To incorporate an efficient activity recognition and abnormality detection system in smart environments, the routine activities of the occupant are modeled and any deviation from the activity model is recognized as abnormality. Recognition systems are generally designed using machine learning strategies and in this paper a novel hybrid, data and knowledge-driven approach is introduced. Markov Logic Network (MLN) used in our design is a suitable approach for activity recognition as it integrates common sense knowledge with a probabilistic model that augments the recognition ability of the system. The proposed activity recognition system for dementia care uses a hierarchical approach to detect abnormality in occupant behavior using MLN. The abnormality in the context of dementia care is identified in terms of factors associated with the activity such as objects, location, time and duration. The task of recognition is done in a hierarchical manner based on the priority of the factor that is associated with each layer. The motivation for designing a hierarchical approach was to enable each layer to commence its computation after inferring from the lower layers that the ongoing activity was normal with regard to the associated factors. This hierarchical feature enables quick decisions, as factors that require immediate attention are processed first at the lowest layer. Experimental results indicate that the hierarchical approach has higher accuracy in recognition and efficient response time when compared to the existing approaches.

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Sensor data fusion for activity monitoring in the PERSONA ambient assisted living project

Cited by 46 publications

References 37 publications

The OCarePlatform: A context-aware system to support independent living

The OCarePlatform: A context-aware system to support independent living

An image segmentation algorithm based on improved multiscale random field model in wavelet domain

Hierarchical activity recognition for dementia care using Markov Logic Network

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