“SonicDoor: A Person Identification System Based on Modeling of Shape, Behavior and Walking Patterns” by Nacer Khalil, Driss Benhaddou, Omprakash Gnawali, and Jaspal Subhlok. ACM Transactions on Sensor Networks (TOSN) , vol. 14 , no. 3-4 , Dec. 2018.
Non-intrusive occupant identification enables numerous applications in Smart Buildings such as personalization of climate and lighting. Current techniques do not scale beyond 20 people whereas commercial buildings have 100 or more people. This paper proposes a new method to identify occupants by sensing their body shape, movement and walking patterns as they walk through a SonicDoor, a door instrumented with three ultrasonic sensors. The proposed method infers contextual information such as paths and historical walks through different doors of the building. Each SonicDoor is instrumented with ultrasonic ping sensors, one on top sensing height and two on the sides of the door sensing width of the person walking through the door. SonicDoor detects a walking event and analyzes it to infer whether the Walker is using a phone, holding a handbag, or wearing a backpack. It extracts a set of features from the walking event and corrects them using a set of transformation functions to mitigate the bias. We deployed five SonicDoors in a real building for two months and collected data consisting of over 9000 walking events spanning over 170 people. The proposed method identifies 100 occupants with an accuracy of 90.2%, which makes it suitable for commercial buildings.
BibTeX entry:
@article{sonicdoor-tosn2018,
author = {Nacer Khalil, and Driss Benhaddou and Omprakash Gnawali and
Jaspal Subhlok},
title = {SonicDoor: A Person Identification System Based on Modeling of
Shape, Behavior and Walking Patterns},
journal = { ACM Transactions on Sensor Networks (TOSN) },
volume = {14 },
number = {3-4 },
pages = { },
month = dec,
year = {2018}
}