The smart floor: a mechanism for natural user identification and tracking

By R. J. Orr, Gregory D. Abowd. CHI Extended Abstracts, 2000

819 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a69e63ef02a0b421e71d2a3074213c98a06337db

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

A Survey of Indoor Positioning and Object Locating Systems (2010, background): “Some of the outstanding positioning systems are GPS [14], AT&T Cambridge Ultrasonic Bats [3], Microsoft Research’s WaveLAN system [15], Active Badges [16] ,Smart Floor from Georgia Tech [17], Radio tags, Computer vision systems [18], and cellular phone based systems [19].”
The smart thermostat: using occupancy sensors to save energy in homes (2010, background): “Therefore, the smart thermostat does not require cameras or wearable tags that may be considered intrusive to the user [22, 23] or more sophisticated sensing systems used for fine-grained tracking and activity recognition [24, 25, 26].”
A Probabilistic Room Location Service for Wireless Networked Environments (2001, methodology): “The SmartFloor project can identify and track a user using Hidden Markov models based on “footfall” signatures acquired from a floor equipped with load sensors [11].”
Activity Recognition in the Home Using Simple and Ubiquitous Sensors (2004, background): “For instance, a switch sensor in the bed can strongly suggest sleeping [1], and pressure mat sensors can be used for tracking the movement and position of people [22,2].”
Network-based wireless location: challenges faced in developing techniques for accurate wireless location information (2005, background): “Some of the older proposed location-aware systems for indoor environments [53]–[57] require specialized hardware such as ultrasound transmitters, camera, and infrared transmitters.”
Location Systems for Ubiquitous Computing (2001, methodology): “In Georgia Tech’s Smart Floor proximity location system [24], embedded pressure sensors capture footfalls, and the system uses the data for position tracking and pedestrian recognition.”
How smart are our environments? An updated look at the state of the art (2007, background): “For example, the embedded pressure sensors in the Aware Home [63] capture inhabitants' footfalls, and the smart home uses these data for position tracking and pedestrian recognition.”
The Horus location determination system (2008, background): “Examples include GPS [3], wide-area cellular-based systems [30], infraredbased systems [31, 32], ultrasonic-based systems [33], various computer vision systems [34], and physical contact systems [ 35 ].”
A Survey on Ambient-Assisted Living Tools for Older Adults (2013, background): “Smart floor technology uses pressure sensors to detect presence of inhabitants [118].”
The Horus WLAN location determination system (2005, background): “Examples include GPS [11], wide-area cellular-based systems [24], infrared-based systems [4, 26], ultrasonic-based systems [19], various computer vision systems [16], and physical contact systems [18].”
The Gesture Pendant: A Self-illuminating, Wearable, Infrared Computer Vision System for Home Automation Control and Medical Monitoring (2000, background): “To sense the activity in the house, efforts are being focused on creating an infrastructure of sensors and computing within the house, from floors that can identify those who walk on it [11], to RF transmitters that can provide resident location information [9], to cameras and microphones in the cei”
WLAN location determination via clustering and probability distributions (2003, background): “Examples include GPS [7], wide-area cellular-based systems [16], infrared-based systems [18, 3], ultrasonic-based systems [12], various computer vision systems [9], physical contact systems [11], and radio frequency (RF) based systems [4, 20, 14, 5, 13, 10, 8].”
Rapid (2017, background): “In [17], authors leverage force measurement sensors to record the footstep force signal and build corresponding profiles, achieving an accuracy of 90% to 97.”
Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments (2009, background): “For example, in [16], the system uses pressure sensors to detect the presence of a person over floor tiles.”