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- W4383108857 abstract "People detection is widely used in remote health monitoring and smart homes. Passive infrared (PIR) sensors are the most used devices-free detection system for their affordability, non-invasiveness, low power consumption and high accuracy. However, there are still many challenges related to analyzing digital output data to extract useful information with multi-PIR systems. Thus, in this paper we propose a measurement system composed of multi-PIR sensors mounted on the head of a mobile social robot in which the granularity of the overlapped field of view (FoV) of sensors are considered to increase the localization accuracy. In addition, a Decision Tree (DT) classifier algorithm is trained on the system data to improve the localization accuracy. The results show that the accuracy of the system is 96% for tests performed in controlled environments when subjects have gait movement constraints. The accuracy of the system decreases (83.3%) when no constraints are applied." @default.
- W4383108857 created "2023-07-05" @default.
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- W4383108857 date "2023-05-29" @default.
- W4383108857 modified "2023-10-17" @default.
- W4383108857 title "Localization of Older People in an Indoor Scenario: A Measurement System Based on PIR Sensors Installed in a Social Robot" @default.
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- W4383108857 doi "https://doi.org/10.1109/metrolivenv56897.2023.10164045" @default.
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