Simulation of a smart home environment

With the realisation of the Internet of Things (IoT) paradigm, the analysis of the Activities of Daily Living (ADLs), in a smart home environment, is becoming an active research domain. The existence of representative datasets is a key requirement to advance the research in smart home design. Such datasets are an integral part of the visualisation of new smart home concepts as well as the validation and evaluation of emerging machine learning models. Machine learning techniques that can learn ADLs from sensor readings are used to classify, predict and detect anomalous patterns. Such techniques require data that represent relevant smart home scenarios, for training, testing and validation. However, the development of such machine learning techniques is limited by the lack of real smart home datasets, due to the excessive cost of building real smart homes. This paper provides two datasets for classification and anomaly detection. The datasets are generated using OpenSHS, (Open Smart Home Simulator), which is a simulation software for dataset generation. OpenSHS records the daily activities of a participant within a virtual environment. Seven participants simulated their ADLs for different contexts, e.g., weekdays, weekends, mornings and evenings. Eighty-four files in total were generated, representing approximately 63 days worth of activities. Forty-two files of classification of ADLs were simulated in the classification dataset and the other forty-two files are for anomaly detection problems in which anomalous patterns were simulated and injected into the anomaly detection dataset.

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“…Examples of model-based approaches include [6][7][8]. Examples of interactive approaches include [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

SIMADL: Simulated Activities of Daily Living Dataset

Alshammari

Sedky

et al. 2018

Data

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“…In addition, through the repeated execution of the simulation, it is possible to obtain approximate data on factors that negatively affect the performance and accuracy of the system and variations in the real-world. [6]. The simulator which creates the contextdata is composed of a virtual space, virtual sensors and a virtual character.…”

Section: Methodsmentioning

confidence: 99%

“…The experiment results were saved in a database. The researchers are using this data to develop a fall detection algorithm test [6].…”

Section: Related Workmentioning

confidence: 99%

“…The simulators used for user activity recognition algorithms are Persim [7] and the WSN Simulator [6]. And secondly simulators used for service validation using contextawareness are UbiREAL [8] and the Smart house simulator [9].…”

Section: Related Workmentioning

confidence: 99%

“…To remove the kinds of constraints that cause this situation, such as high costs, long duration and the resistance of participants to sensors and wearable devices needed in a real environment, researches can use simulators which make a virtual space and generate data in a relatively short period of time. Much recent progress has occurred in the development of such applications [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The User Activity Reasoning Model in a Virtual Living Space Simulator

Bokyoung¹,

Min²,

Bang³

et al. 2015

IJSEIA

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Linac: A Smart Environment Simulator of Human Activities

Federico

Nikolajsen

Azam

et al. 2022

Lecture Notes in Business Information Processing

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The identification and construction of datasets of human activities is an extremely time-consuming and resource intensive task, yet researchers cannot refrain from such datasets. The publicly available datasets may not reflect all the researchers’ requirements and are not scrupulously documented. In addition, these datasets can cope with just a limited and predefined set of behaviors. To address these challenges, we developed an instrument that allows to simulate the behavior of agents interacting with an environment. The environment is a customized configuration, equipped with sensors. The simulation generates as output a stream of events stemming from activated sensors. In addition, the agents behavior is not fully deterministic, so as to reflect the dynamic nature of human beings and to be as realistic as possible.

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Simulation of a smart home environment

Cited by 15 publications

References 13 publications

SIMADL: Simulated Activities of Daily Living Dataset

SIMADL: Simulated Activities of Daily Living Dataset

The User Activity Reasoning Model in a Virtual Living Space Simulator

Linac: A Smart Environment Simulator of Human Activities

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