An Enhanced Fall Detection Approach in Smart Homes Using Optical Flow and Residual Autoencoder

Elshwemy, Faten

doi:10.30534/ijatcse/2020/170932020

Cited by 2 publications

(1 citation statement)

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“…On the other hand, the depth cameras such as time of flight (TOF) cameras provide both RGB frames and depth information. Depth information is obtained by calculating the distance of object from camera which is represented by depth image pixel value [16] [17].…”

Section: IVmentioning

confidence: 99%

A Taxonomy and Survey of Occupancy Detection Methods for Efficient Space and Energy Utilisation of Buildings

Kadir¹

2020

IJATCSE

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Human factor has a significant influence in space and energy utilisation in buildings. Space utilisation refers to a measure of whether space of a building is being used and how it is being used. While energy utilisation refers to the amount of energy being used. As space and energy of buildings are mostly dominated by the occupancy and related activities, occupancy information is vital in managing space and energy use. By detecting the presence of an occupant in a building or occupancy detection, space and energy can be utilised efficiently by corresponding to the occupant's dimension and activities, thus avoiding wastage. Accordingly, through various methods, occupancy detection employs various sensors available in term of spatial temporal properties depending on the area to be deployed. Majority of past studies were focused on the aspect of energy utilisation and occupancy and there is lack of review paper which gathers information on occupancy detection inrelation to both space and energy utilisation.Therefore, this paper aims to develop a taxonomy of occupancy detection technologies for efficient space and energy utilisation of buildings. A survey of past studies on space and energy utilisation of buildings in relation to occupancy level are done to understand the relationship between occupancy and space and energy utilisation, respectively. A taxonomy is then built to identify the technologies which are currently available for occupancy detection.

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Section: IVmentioning

confidence: 99%