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They have been shown to become helpful in domains including reaching consensus for group choice creating [16] or extending information queries to allow the search for [http://www.musicpella.com/members/octaveflax00/activity/610959/ Prosody cues [39] which convey details about grammar [40]. Typical consonant-identification thresholds, especially] incomplete results [17]. However, these atomic actions (sub-activities) are input [https://dx.doi.org/10.1111/j.1399-3046.2011.01563.x title= j.1399-3046.2011.01563.x] to the knowledge-based method composed by the fuzzy ontology, which supplies the outcomes in the data-driven module with semantics and performs ontology reasoning to infer a lot more abstract and complex activities, i.e., cooking, washing dishes, etc. By using a fuzzy ontology, the advantages of expressibility and looseness in the activity models allow a higher tolerance towards the inherent uncertainty and vagueness of the problem. Within the experimental section, we validate our approach over a benchmark dataset (Cornell Activity Dataset, CAD-120). In summary, our proposal's contributions can be enumerated as: ?The capacity to handle imprecise, vague, uncertain or incomplete information in ontological AR, for instance missing sensor readings, in real-time environments. This model is validated having a public dataset of complex activities containing 3D-depth user and objects position information (i.e., it doesn't requireSensors 2014,????utilizing wearable sensors). This dataset contains activities inside a continuous video data stream. Then, a user tracking method is required. A method that simplifies complexity within the education phase. Inside the case on the new addition/removal/replacement of input information, as opposed to re-training, it is actually only needed to modify the impacted activity rule in the fuzzy ontology, since the activities and their relationships can be modeled as widespread sense rules.Ation of human actions is key to achieving a [https://dx.doi.org/10.1186/1297-9686-43-23 title= 1297-9686-43-23] appropriate activity recognition. As a consequence, all of this facts must be taken into account in the reasoning and recognition process. Fuzzy ontologies have great advantages with respect to crisp ones [5,15]. They have been shown to become beneficial in domains for instance reaching consensus for group choice generating [16] or extending data queries to let the search for incomplete results [17]. Inside the domain of activity recognition, they permit one particular to define that an activity, i.e., CoffeeBreak, is recognized, taking into account the relevance of its involved sub-activities employing weighted aggregation (e.g., 0.3 TakeMug, 0.3 TakeCoffeePan, 0.four TakeMilk). Therefore, when a sub-activity has been skipped due to an exception (e.g., milk operating out) or perhaps a missing sensor reading, the activity can nonetheless be recognized with a lower degree of reliability. In contrast, the exact same activity, if it truly is formalized in a crisp ontology, couldn't be recognized if any with the exclusive sub-activities are missing. In a earlier work, we've got demonstrated theoretically that fuzzy ontologies is usually employed to improve the accuracy in activity recognition scenarios with respect to crisp models [5]. In this paper, we extend the preceding function [5] and produce a hybrid fuzzy ontology/data-driven strategy that embraces the benefits of each methodologies. Around the a single hand, a data-driven model is in charge of processing sensor information straight to recognize atomic human activities, i.e., walk, take/release an object, sit down, and so on. We contact these activities atomic due to the fact they can't be explained as a sequence of easier activities and are often performed inside the same way.
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Inside the domain of [http://hs21.cn/comment/html/?289487.html Re-based system consist of: (1) functions are hard to extract in some situations] activity recognition, they let a single to define that an activity, i.e., CoffeeBreak, is recognized, taking into account the relevance of its involved sub-activities making use of weighted aggregation (e.g., 0.three TakeMug, 0.3 TakeCoffeePan, 0.four TakeMilk). Hence, it [https://dx.doi.org/10.1111/j.1551-6709.2011.01192.x title= j.1551-6709.2011.01192.x] is just not essential to carry out re-training in the data-driven model for its adaptation to new situations with the very same activity. On the other hand, these atomic actions (sub-activities) are input [https://dx.doi.org/10.1111/j.1399-3046.2011.01563.x title= j.1399-3046.2011.01563.x] towards the knowledge-based technique composed by the fuzzy ontology, which offers the outcomes in the data-driven module with semantics and performs ontology reasoning to infer a lot more abstract and complicated activities, i.e., cooking, washing dishes, etc. By using a fuzzy ontology, the benefits of expressibility and looseness in the activity models let a larger tolerance for the inherent uncertainty and vagueness in the challenge. Inside the experimental section, we validate our approach over a benchmark dataset (Cornell Activity Dataset, CAD-120). In summary, our proposal's contributions can be enumerated as: ?The ability to deal with imprecise, vague, uncertain or incomplete information in ontological AR, for example missing sensor readings, in real-time environments. This model is validated with a public dataset of complicated activities containing 3D-depth user and objects position data (i.e., it doesn't requireSensors 2014,????applying wearable sensors). This dataset includes activities in a continuous video information stream. Then, a user tracking technique is required. A process that simplifies complexity inside the training phase. Thus, when a sub-activity has been skipped as a result of an exception (e.g., milk running out) or a missing sensor reading, the activity can nonetheless be recognized with a decrease degree of reliability. In contrast, exactly the same activity, if it can be formalized within a crisp ontology, could not be recognized if any in the exclusive sub-activities are missing. Within a previous perform, we have demonstrated theoretically that fuzzy ontologies might be made use of to improve the accuracy in activity recognition scenarios with respect to crisp models [5]. In this paper, we extend the previous work [5] and develop a hybrid fuzzy ontology/data-driven approach that embraces the rewards of both methodologies. On the a single hand, a data-driven model is in charge of processing sensor information straight to recognize atomic human activities, i.e., walk, take/release an object, sit down, and so on. We contact these activities atomic considering that they can't be explained as a sequence of simpler activities and are always performed within the very same way. Therefore, it [https://dx.doi.org/10.1111/j.1551-6709.2011.01192.x title= j.1551-6709.2011.01192.x] isn't essential to execute re-training with the data-driven model for its adaptation to new instances from the exact same activity. On the other hand, these atomic actions (sub-activities) are input [https://dx.doi.org/10.1111/j.1399-3046.2011.01563.x title= j.1399-3046.2011.01563.x] to the knowledge-based system composed by the fuzzy ontology, which provides the results in the data-driven module with semantics and performs ontology reasoning to infer more abstract and complex activities, i.e., cooking, washing dishes, and so on. By using a fuzzy ontology, the advantages of expressibility and looseness in the activity models enable a higher tolerance for the inherent uncertainty and vagueness with the difficulty.

Aktuelle Version vom 21. März 2018, 13:13 Uhr

Inside the domain of Re-based system consist of: (1) functions are hard to extract in some situations activity recognition, they let a single to define that an activity, i.e., CoffeeBreak, is recognized, taking into account the relevance of its involved sub-activities making use of weighted aggregation (e.g., 0.three TakeMug, 0.3 TakeCoffeePan, 0.four TakeMilk). Hence, it title= j.1551-6709.2011.01192.x is just not essential to carry out re-training in the data-driven model for its adaptation to new situations with the very same activity. On the other hand, these atomic actions (sub-activities) are input title= j.1399-3046.2011.01563.x towards the knowledge-based technique composed by the fuzzy ontology, which offers the outcomes in the data-driven module with semantics and performs ontology reasoning to infer a lot more abstract and complicated activities, i.e., cooking, washing dishes, etc. By using a fuzzy ontology, the benefits of expressibility and looseness in the activity models let a larger tolerance for the inherent uncertainty and vagueness in the challenge. Inside the experimental section, we validate our approach over a benchmark dataset (Cornell Activity Dataset, CAD-120). In summary, our proposal's contributions can be enumerated as: ?The ability to deal with imprecise, vague, uncertain or incomplete information in ontological AR, for example missing sensor readings, in real-time environments. This model is validated with a public dataset of complicated activities containing 3D-depth user and objects position data (i.e., it doesn't requireSensors 2014,????applying wearable sensors). This dataset includes activities in a continuous video information stream. Then, a user tracking technique is required. A process that simplifies complexity inside the training phase. Thus, when a sub-activity has been skipped as a result of an exception (e.g., milk running out) or a missing sensor reading, the activity can nonetheless be recognized with a decrease degree of reliability. In contrast, exactly the same activity, if it can be formalized within a crisp ontology, could not be recognized if any in the exclusive sub-activities are missing. Within a previous perform, we have demonstrated theoretically that fuzzy ontologies might be made use of to improve the accuracy in activity recognition scenarios with respect to crisp models [5]. In this paper, we extend the previous work [5] and develop a hybrid fuzzy ontology/data-driven approach that embraces the rewards of both methodologies. On the a single hand, a data-driven model is in charge of processing sensor information straight to recognize atomic human activities, i.e., walk, take/release an object, sit down, and so on. We contact these activities atomic considering that they can't be explained as a sequence of simpler activities and are always performed within the very same way. Therefore, it title= j.1551-6709.2011.01192.x isn't essential to execute re-training with the data-driven model for its adaptation to new instances from the exact same activity. On the other hand, these atomic actions (sub-activities) are input title= j.1399-3046.2011.01563.x to the knowledge-based system composed by the fuzzy ontology, which provides the results in the data-driven module with semantics and performs ontology reasoning to infer more abstract and complex activities, i.e., cooking, washing dishes, and so on. By using a fuzzy ontology, the advantages of expressibility and looseness in the activity models enable a higher tolerance for the inherent uncertainty and vagueness with the difficulty.

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