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Erschienen in: Neural Processing Letters 2/2020

07.01.2020

Daily Activity Feature Selection in Smart Homes Based on Pearson Correlation Coefficient

verfasst von: Yaqing Liu, Yong Mu, Keyu Chen, Yiming Li, Jinghuan Guo

Erschienen in: Neural Processing Letters | Ausgabe 2/2020

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Abstract

In the case of a smart home, the ability to recognize daily activities depends primarily on the strategy used for selecting the appropriate features related to these activities. To achieve the goal, this paper presents a daily activity feature selection strategy based on the Pearson Correlation Coefficient. Firstly, a daily activity feature is viewed as a vector in Pearson Correlation Coefficient formula. Secondly, the relation degree between daily activity features is obtained according to weighted Pearson Correlation Coefficient formula. At last, redundant features are removed by the relation degree between daily activity features. Two distinct datasets are adopted to mitigate the effects of the coupling of the dataset used and the sensor configuration. Three different machine learning techniques are employed to evaluate the performance of the proposed approach in activity recognition. The experiment results show that the proposed approach yields higher recognition rates and achieves average improvement F-measures of 1.56% and 2.7%, respectively.

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Metadaten
Titel
Daily Activity Feature Selection in Smart Homes Based on Pearson Correlation Coefficient
verfasst von
Yaqing Liu
Yong Mu
Keyu Chen
Yiming Li
Jinghuan Guo
Publikationsdatum
07.01.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-019-10185-8

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