Smart Floor for a more comfortable and safer life

Haraburda, Karol; Czygier, Jakub; Recko, Maciej

doi:10.1109/yef-ece.2019.8740809

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Current technology trends require a higher presence of pressure sensors integrated into their surroundings with the aim to increase the degree of automation or to enable condition monitoring [ 1 , 2 , 3 ]. Be it for medical applications [ 4 ], industrial environments [ 1 ], positional tracking [ 2 , 5 , 6 , 7 ], or other fields [ 8 , 9 ], pressure sensors have become ubiquitous.…”

Section: Introductionmentioning

confidence: 99%

Study of Pressure Distribution in Floor Tiles with Printed P(VDF:TrFE) Sensors for Smart Surface Applications

Rueda

Schäffner

Petritz

et al. 2023

Sensors

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Pressure sensors integrated in surfaces, such as the floor, can enable movement, event, and object detection with relatively little effort and without raising privacy concerns, such as video surveillance. Usually, this requires a distributed array of sensor pixels, whose design must be optimized according to the expected use case to reduce implementation costs while providing sufficient sensitivity. In this work, we present an unobtrusive smart floor concept based on floor tiles equipped with a printed piezoelectric sensor matrix. The sensor element adds less than 130 µm in thickness to the floor tile and offers a pressure sensitivity of 36 pC/N for a 1 cm2 pixel size. A floor model was established to simulate how the localized pressure excitation acting on the floor spreads into the sensor layer, where the error is only 1.5%. The model is valuable for optimizing the pixel density and arrangement for event and object detection while considering the smart floor implementation in buildings. Finally, a demonstration, including wireless connection to the computer, is presented, showing the viability of the tile to detect finger touch or movement of a metallic rod.

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Section: Introductionmentioning

confidence: 99%

Study of Pressure Distribution in Floor Tiles with Printed P(VDF:TrFE) Sensors for Smart Surface Applications

Rueda

Schäffner

Petritz

et al. 2023

Sensors

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show abstract

“…An issue with this capacitive approach is that it requires pressure-based floor deformation to operate, introducing fatigue-based longevity concerns similar to pressure-based sensor implementations. Hoffmann et al [ 9 ] explored gait mode classification using a capacitive sensing floor and an LSTM network, Other measurement methods have been used to detect: gender [ 39 , 40 ]; gait on steps [ 41 ]; emotional, height, and criminal detection [ 42 ]; fatigue [ 43 ]; identity [ 44 ]; and footsteps [ 21 , 45 ] and spatio-temporal gait parameters [ 46 ]. However, these approaches either required the subject to be tagged with a device, required the detection of floor vibration which would vary as the flooring aged, or suffered from low accuracy.…”

Section: Introductionmentioning

confidence: 99%

Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks

Konings

Alam

Faulkner

et al. 2022

Sensors

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In recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.

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Smart Floor for a more comfortable and safer life

Cited by 2 publications

References 8 publications

Study of Pressure Distribution in Floor Tiles with Printed P(VDF:TrFE) Sensors for Smart Surface Applications

Study of Pressure Distribution in Floor Tiles with Printed P(VDF:TrFE) Sensors for Smart Surface Applications

Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks

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