SOnAr: Smart Ontology activity recognition framework to fulfill semantic web in smart homes

Zolfaghari, Samaneh; Zall, Raziyeh; Keyvanpour, Mohammad Reza

doi:10.1109/icwr.2016.7498458

Cited by 14 publications

(8 citation statements)

References 27 publications

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“…"Aging in place" for an elderly person is one key element in ambient assisted living (AAL) technologies [1]. For recognition [2][3][4][5][6][7][8][9][10][11][12][13][14][15] and classification of ADL [16,17] are used various mathematical methods such as Hidden Markov Model (HMM), Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) [18,6], Artificial Neural Networks (ANN) [11] or adaptive-network-based fuzzy inference system (ANFIS) [19,20]. For detection of ADL in SHC it is possible to use RFID [21], PIR [22], CO 2 [23] sensors or presence sensors, on the basis of which probability models of the people's behavior in SH [24] can be built, respecting the privacy [25] of SHC residents [26].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of the daily living activities in smart home care

Vaňuš

Belesova

Martínek

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO 2 , humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO 2 concentration measurement for detecting and monitoring room´s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg-Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO 2 concentrations from obtained measurements of temperature (indoor, outdoor) T i , T o and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and technical functions (unregulated, uncontrolled) in an experimental Smart Home (SH), LMA was trained through the data picked up by the sensors of CO 2 , T and rH with the aim to indirectly predict CO 2 leading to the elimination of CO 2 sensor from the measurement process. Within the realized experiment, input parameters of the neuronal network and the number of neurons for LMA were optimized on the basis of calculated values of Root Mean Squared Error, the correlative coefficient (R) and the length of the measured training time ANN. With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor. Experimental results verified high method accuracy (>95%) within the short-term and long-term experiments for learned ANN (1.6.2015ANN (1.6. -30.6.2015. For learned ANN (1.2.2014ANN (1.2. -27.2.2014) was verified worse method accuracy (>60%). The original contribution is a verification of a low-cost method for the detection of human presence in the real operating environment of SHC. In the third part of the paper is described the practical implementation of voice control of operating technical functions by the KNX technology in SHC by means of the in-house developed application HESTIA, intended for both the desktop system version and the mobile version of the Windows 10 operating system for mobile phones. The resultant application can be configured for any building equipped with the KNX bus system. Voice control implementation is an in-house solution, no third-party software is used here. Utilization of the voice communication application in SHC was proven on the experimental basis with the combination of measurement CO 2 for ADL monitoring in SHC.

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Section: Introductionmentioning

confidence: 99%

Monitoring of the daily living activities in smart home care

Vaňuš

Belesova

Martínek

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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“…Noise Effect: In general, sensory data are inherently noisy and has untrustworthy nature which leads to lack of reliability in the Bottom-Up, Top-Down and Hybrid approaches [29] [31]. As mentioned before, there is possibility of noise existence in annotation process too and lead to accuracy reduction, increase computational complexity and classification error in activity recognition unless some actions such as what is done in [24] using hybrid approaches have been considered.…”

Section: A Proposed Measuresmentioning

confidence: 99%

“…[32]. For example, to detect activity "clean up" which is a complex activity, recognition in the form of the simpler components carried out and the following axioms represented in (4) and (5) are added to the knowledge base [31].…”

Section: ) Description-based Activity Modelingmentioning

confidence: 99%

“…The Descriptionbased activity modeling represented activity as an object and models activities as a hierarchy of classes where each class can be described by a number of properties so these approaches including a set of representational concepts [4] [31]. The generated activity models are able to capture built-in interrelations between objects and activities such as proposed method in Fig.…”

Section: ) Description-based Activity Modelingmentioning

confidence: 99%

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SARF: Smart Activity Recognition Framework in Ambient Assisted Living

Zolfaghari

Keyvanpour

2016

Annals of Computer Science and Information Systems

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Abstract-Human activity recognition in Ambient Assisted Living (AAL) is an important application in health care systems and allows us to track regular activities or even predict these activities in order to monitor healthcare and find changes in patterns and lifestyles. A review of the literature reveals various approaches to discovering and recognizing human activities. The presence of a vast number of activity recognition issues and approaches has made it difficult to make adequate comparisons and accurate assessment. Introducing the five basic components of activity recognition in the smart homes as a famous environment to remote monitoring of patients and independent living for elderly, the present paper proposes SARF framework to classify each of activity recognition approaches and then it is evaluated based on the proposed classification by some proposed measures. Using SARF proposed framework can play an effective role in selecting the appropriate method for human activity recognition in smart homes and beneficial in analysis and evaluation of different methods for various challenges in this field.

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“…Some of the main concerns with the rising aging population are that the degradation of quality of life and the care receiving/providing by health care services. The goal of Ambient Assistive Living (AAL) systems [2]- [4] is to provide assistive tools to target those concerns.…”

Section: Introductionmentioning

confidence: 99%

Real-time sensor observation segmentation for complex activity recognition within smart environments

Triboan

Chen

et al. 2017

2017 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computed, Scalable Computing &Amp; Commun

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Activity Recognition (AR) is at the heart of any types of assistive living systems. One of the key challenges faced in AR is segmentation of the sensor events when inhabitant performs simple or composite activities of daily living (ADLs). In addition, each inhabitant may follow a particular ritual or a tradition in performing different ADLs and their patterns may change overtime. Many recent studies apply methods to segment and recognise generic ADLs performed in a composite manner. However, little has been explored in semantically distinguishing individual sensor events and directly passing it to the relevant ongoing/new atomic activities. This paper proposes to use the ontological model to capture generic knowledge of ADLs and methods which also takes inhabitant-specific preferences into considerations when segmenting sensor events. The system implementation was developed, deployed and evaluated against 84 use case scenarios. The result suggests that all sensor events were adequately segmented with 98% accuracy and the average classification time of 3971ms and 62183ms for single and composite ADL scenarios were recorded, respectively.

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SOnAr: Smart Ontology activity recognition framework to fulfill semantic web in smart homes

Cited by 14 publications

References 27 publications

Monitoring of the daily living activities in smart home care

Monitoring of the daily living activities in smart home care

SARF: Smart Activity Recognition Framework in Ambient Assisted Living

Real-time sensor observation segmentation for complex activity recognition within smart environments

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