A Feature-Based Knowledge Transfer Framework for Cross-Environment Activity Recognition Toward Smart Home Applications

Chiang, Yi-Ting; Lu, Ching-Hu; Hsu, Jane Yung-jen

doi:10.1109/thms.2016.2641679

Cited by 40 publications

(17 citation statements)

References 18 publications

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“…As data is collected, a transfer learning approach is used to pass the data to the smart home [20]. Chiang et al [21] proved that without any target data (i.e., no data), the amount of transferred knowledge is insufficient, but it can be increased by using a small amount of labelled data.…”

Section: State-of-the-artmentioning

confidence: 99%

A user-guided personalization methodology to facilitate new smart home occupancy

Ali

Augusto

Windridge

et al. 2022

Univ Access Inf Soc

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Smart homes are becoming increasingly popular in providing people with the services they desire. Activity recognition is a fundamental task to provide personalised home facilities. Many promising approaches are being used for activity recognition; one of them is data-driven. It has some fascinating features and advantages. However, there are drawbacks such as the lack of ability to providing home automation from the day one due to the limited data available. In this paper, we propose an approach, called READY (useR-guided nEw smart home ADaptation sYstem) for developing a personalised automation system that provides the user with smart home services the moment they move into their new house. The system development process was strongly user-centred, involving users in every step of the system’s design. Later, the user-guided transfer learning approach was introduced that uses an old smart home data set to enhance the existing smart home service with user contributions. Finally, the proposed approach and designed system were tested and validated in the smart lab that showed promising results.

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Section: State-of-the-artmentioning

confidence: 99%

A user-guided personalization methodology to facilitate new smart home occupancy

Ali

Augusto

Windridge

et al. 2022

Univ Access Inf Soc

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“…Human behavior has recently studied in some smart city applications, notably smart buildings [15,16]. Several solutions have been proposed ranging from technical solutions based on pervasive computing for optimal monitoring [17][18][19], energy consumption analysis [20], and the use of ML for human for occupancy estimation [21], and activity recognition [22,23]. A detailed review on all these solutions is available in [8].…”

Section: Related Workmentioning

confidence: 99%

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection

et al. 2021

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This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories. First, algorithms based on data mining and knowledge discovery, which study the different correlation among human behavioral data, and identify the collective abnormal human behavior from knowledge extracted. Secondly, algorithms exploring convolution deep neural networks, which learn different features of historical data to determine the collective abnormal human behaviors. Experiments on an actual human behaviors database have been carried out to demonstrate the usefulness of the proposed algorithms. The results show that the deep learning solution outperforms both data mining as well as the state-of-the-art solutions in terms of runtime and accuracy performance. In particular, for large datasets, the accuracy of the deep learning solution reaches 88%, however other solutions do not exceed 81%. Additionally, the runtime of the deep learning solution is below 50 seconds, whereas other solutions need more than 80 seconds for analyzing the same database.

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“…To reduce the complexity of knowledge transfer across different domains, Chiang et al [7] focused on exploring the differences caused by the ambient sensors and the target domain, proposed a framework that knowledge transfer that uses standard SVM (support vector machine) and RBF (radial basis function). However, in their proposal, only single-resident scenario was considered.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building

Peng

Koshizuka

2021

Applied Sciences

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Smart building, one of IoT-based emerging applications is where energy-efficiency, human comfort, automation, security could be managed even better. However, at the current stage, a unified and practical framework for knowledge inference inside the smart building is still lacking. In this paper, we present a practical proposal of knowledge extraction on event-conjunction for automatic control in smart buildings. The proposal consists of a unified API design, ontology model, inference engine for knowledge extraction. Two types of models: finite state machine(FSMs) and bayesian network (BN) have been used for capturing the state transition and sensor data fusion. In particular, to solve the problem that the size of time interval observations between two correlated events was too small to be approximated for estimation, we utilized the Markov Chain Monte Carlo (MCMC) sampling method to optimize the sampling on time intervals. The proposal has been put into use in a real smart building environment. 78-days data collection of the light states and elevator states has been conducted for evaluation. Several events have been inferred in the evaluation, such as room occupancy, elevator moving, as well as the event conjunction of both. The inference on the users’ waiting time of elevator-using revealed the potentials and effectiveness of the automatic control on the elevator.

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A Feature-Based Knowledge Transfer Framework for Cross-Environment Activity Recognition Toward Smart Home Applications

Cited by 40 publications

References 18 publications

A user-guided personalization methodology to facilitate new smart home occupancy

A user-guided personalization methodology to facilitate new smart home occupancy

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection

Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building

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