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User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.

Bourobou ST, Yoo Y - Sensors (Basel) (2015)

Bottom Line: In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations.The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms.Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, Pusan National University, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 609-735, Korea. thomaserge@yahoo.fr.

ABSTRACT
This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

No MeSH data available.


Training smart environment for activities recognition.
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sensors-15-11953-f010: Training smart environment for activities recognition.

Mentions: The ANN algorithm is characterized by its capability to efficiently recognize and predict user activity, to perform computation efficiently despite the large volume of dataset, whereas other classification techniques such as Hidden Markov Model (HMM), Naive Bayes, and C4.5 face challenges in terms of the runtime and interleaving events. On the other hand, the ANN algorithm is widely used for temporal and spatial relationship identification. Thus, as we said previously, besides the ANN algorithm, the J48 decision tree is added in order to overcome the irrelevance and redundancy of features that can significantly increase the computational complexity and classification errors of the algorithms, especially ANN. Figure 10 illustrates the training for a smart home system to recognize and predict user activities using ANN based on the Allen’s temporal relations.


User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.

Bourobou ST, Yoo Y - Sensors (Basel) (2015)

Training smart environment for activities recognition.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481973&req=5

sensors-15-11953-f010: Training smart environment for activities recognition.
Mentions: The ANN algorithm is characterized by its capability to efficiently recognize and predict user activity, to perform computation efficiently despite the large volume of dataset, whereas other classification techniques such as Hidden Markov Model (HMM), Naive Bayes, and C4.5 face challenges in terms of the runtime and interleaving events. On the other hand, the ANN algorithm is widely used for temporal and spatial relationship identification. Thus, as we said previously, besides the ANN algorithm, the J48 decision tree is added in order to overcome the irrelevance and redundancy of features that can significantly increase the computational complexity and classification errors of the algorithms, especially ANN. Figure 10 illustrates the training for a smart home system to recognize and predict user activities using ANN based on the Allen’s temporal relations.

Bottom Line: In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations.The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms.Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, Pusan National University, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 609-735, Korea. thomaserge@yahoo.fr.

ABSTRACT
This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

No MeSH data available.