Regression Analysis for Gesture Recognition Using RFID Technology

Bouchard, Kévin; Bouchard, Bruno; Bouzouane, Abdenour

doi:10.1007/978-3-319-14424-5_13

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…Application in SBs: Chen et al [120] used the regression technique of orthogonal matching pursuit algorithm to identify the physical and environmental parameters that providing the energy efficiency in an SB. Bouchard et al [121] presented a gesture recognition system using linear regression combined with the correlation coefficient to recognize the gesture direction and estimate the segmentation of continuing gestures of daily usage activities in a smart environment. j) Ensemble methods: A combination of multiple classifiers often referred to as a classifier ensemble, group of classification models that are trained separately and the predictions of those models are then combined in a way to produce the overall prediction [154].…”

Section: I) Regressionmentioning

confidence: 99%

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

et al. 2019

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Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents' experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contains valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.

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Section: I) Regressionmentioning

confidence: 99%