Rule-Based Real-Time ADL Recognition in a Smart Home Environment

Baryannis, George; Woznowski, Przemyslaw; Antoniou, Grigoris

doi:10.1007/978-3-319-42019-6_21

Cited by 7 publications

(10 citation statements)

References 14 publications

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“…All defined properties are flat, i.e., without any special feature (inverse, functional, etc. ), because the identification of ADL is done in an external, ad hoc rules system [28] implemented in Jess, a Java rule-based system. e ontology proposed in [7] is oriented towards the development of an ADL monitoring system which can interact with the users through mobile networks.…”

Section: Related Workmentioning

confidence: 99%

“…( [28], for example, contains one hundred sixty-five predefined activities and thirty-three different types of predefined actions or "atomic activities. "…”

Section: Framework Architecturementioning

confidence: 99%

“…Most of the ontologies available in the literature incorporate concepts and properties to describe ADL from different points of view [6,28]. However, for efficiency reasons, the core ontology that we propose only contains the minimum entities necessary to represent the activities as sequences of events produced by the sensors, as previously described.…”

Section: Extensions Of the Core Ontologymentioning

confidence: 99%

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Ontology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning Techniques

Salguero

Delatorre

Medina

et al. 2019

Scientific Programming

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The miniaturization and price reduction of sensors have encouraged the proliferation of smart environments, in which multitudinous sensors detect and describe the activities carried out by inhabitants. In this context, the recognition of activities of daily living has represented one of the most developed research areas in recent years. Its objective is to determine what daily activity is developed by the inhabitants of a smart environment. In this field, many proposals have been presented in the literature, many of them being based on ad hoc ontologies to formalize logical rules, which hinders their reuse in other contexts. In this work, we propose the use of class expression learning (CEL), an ontology-based data mining technique, for the recognition of ADL. This technique is based on combining the entities in the ontology, trying to find the expressions that best describe those activities. As far as we know, it is the first time that this technique is applied to this problem. To evaluate the performance of CEL for the automatic recognition of activities, we have first developed a framework that is able to convert many of the available datasets to all the ontology models we have found in the literature for dealing with ADL. Two different CEL algorithms have been employed for the recognition of eighteen activities in two different datasets. Although all the available ontologies in the literature are focused on the description of the context of the activities, the results show that the sequence of the events produced by the sensors is more relevant for their automatic recognition, in general terms.

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Section: Related Workmentioning

confidence: 99%

“…( [28], for example, contains one hundred sixty-five predefined activities and thirty-three different types of predefined actions or "atomic activities. "…”

Section: Framework Architecturementioning

confidence: 99%

Section: Extensions Of the Core Ontologymentioning

confidence: 99%

See 1 more Smart Citation

Ontology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning Techniques

Salguero

Delatorre

Medina

et al. 2019

Scientific Programming

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show abstract

“…One or more of these smart appliances collectively achieve some activity in the smart home environment, called ADL (activity of daily living). It is possible to monitor and identify the ADLs in smart home environments [14]. With the help of smart home technology, it is possible to find the amount of load each ADL puts on the grid, and also the allowed time window during which the ADL would perform the activity (e.g., scheduling a washing machine for an hour to clean the clothes anytime between 3PM to 6PM).…”

Section: B Demand-side Management (Dsm) Problemmentioning

confidence: 99%

A unified decision making framework for supply and demand management in microgrid networks

Bharadwaj

Danda²,

Narayanam³

et al. 2018

2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

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This paper considers two important problems -on the supply-side and demand-side respectively and studies both in a unified framework. On the supply side, we study the problem of energy sharing among microgrids with the goal of maximizing profit obtained from selling power while at the same time not deviating much from the customer demand. On the other hand, under shortage of power, this problem becomes one of deciding the amount of power to be bought with dynamically varying prices. On the demand side, we consider the problem of optimally scheduling the time-adjustable demand -i.e., of loads with flexible time windows in which they can be scheduled. While previous works have treated these two problems in isolation, we combine these problems together and provide a unified Markov decision process (MDP) framework for these problems. We then apply the Q-learning algorithm, a popular model-free reinforcement learning technique, to obtain the optimal policy. Through simulations, we show that the policy obtained by solving our MDP model provides more profit to the microgrids.

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“…Unlike data‐driven vision‐based studies on actions, sensor‐based studies have been conducted on actions based on knowledge‐driven approaches. By interpreting sensor data, these studies have aimed to recognize the activities of daily living (ADL) in smart homes , such as BrushTeeth , WashHands , MakeTea , and TakeMedicine . The knowledge‐driven methods for activity modeling and inference have used domain knowledge and heuristics with logic‐based or ontology‐based knowledge engineering methodologies.…”

Section: Related Workmentioning

confidence: 99%

Extensible Hierarchical Method of Detecting Interactive Actions for Video Understanding

Moon¹,

Jin²,

Kwon³

et al. 2017

ETRI Journal

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For video understanding, namely analyzing who did what in a video, actions along with objects are primary elements. Most studies on actions have handled recognition problems for a well-trimmed video and focused on enhancing their classification performance. However, action detection, including localization as well as recognition, is required because, in general, actions intersect in time and space. In addition, most studies have not considered extensibility for a newly added action that has been previously trained. Therefore, proposed in this paper is an extensible hierarchical method for detecting generic actions, which combine object movements and spatial relations between two objects, and inherited actions, which are determined by the related objects through an ontology and rule based methodology. The hierarchical design of the method enables it to detect any interactive actions based on the spatial relations between two objects. The method using object information achieves an F-measure of 90.27%. Moreover, this paper describes the extensibility of the method for a new action contained in a video from a video domain that is different from the dataset used.

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Rule-Based Real-Time ADL Recognition in a Smart Home Environment

Cited by 7 publications

References 14 publications

Ontology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning Techniques

Ontology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning Techniques

A unified decision making framework for supply and demand management in microgrid networks

Extensible Hierarchical Method of Detecting Interactive Actions for Video Understanding

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