18 research outputs found
Analysis of spatio-temporal representations for robust footstep recognition with deep residual neural networks
IEEE: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.”Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering a large cohort of impostors and a small set of clients to verify the reliability of biometric systems. We provide experimental results in 3 critical data-driven security scenarios, according to the amount of footstep data made available for model training: at airports security checkpoints (smallest training set), workspace environments (medium training set) and home environments (largest training set). We report state-of-the-art footstep recognition rates with an optimal equal false acceptance and false rejection rate of 0.7% (equal error rate), an improvement ratio of 371% from previous state-of-the-art. We perform a feature analysis of deep residual neural networks showing effective clustering of client's footstep data and provide insights of the feature learning process.This work has been partially supported by Cognimetrics TEC2015-70627-R MINECO/FEDE
Spatial footstep recognition by convolutional neural networks for biometrie applications
We propose a Convolutional Neural Network model
to learn spatial footstep features end-to-end from a floor sensor
system for biometric applications. Our model’s generalization
performance is assessed by independent validation and evaluation
datasets from the largest footstep database to date, containing
nearly 20,000 footstep signals from 127 users. We report footstep
recognition performance as Equal Error Rate in the range of
9% to 13% depending on the test set. This improves previously
reported footstep recognition rates in the spatial domain up to
4% EE
Neurosymbolic Programming for Science
Neurosymbolic Programming (NP) techniques have the potential to accelerate
scientific discovery. These models combine neural and symbolic components to
learn complex patterns and representations from data, using high-level concepts
or known constraints. NP techniques can interface with symbolic domain
knowledge from scientists, such as prior knowledge and experimental context, to
produce interpretable outputs. We identify opportunities and challenges between
current NP models and scientific workflows, with real-world examples from
behavior analysis in science: to enable the use of NP broadly for workflows
across the natural and social sciences.Comment: Neural Information Processing Systems 2022 - AI for science worksho
Legendre transform in the thermodynamics of flowing polymer solutions
We propose a Legendre transform linking two different choices of nonequilibrium variables (viscous pressure tensor and configuration tensor) in the thermodynamics of flowing polymer solutions. This may avoid some current confusions in the analysis of thermodynamic effects in polymer solutions under flow
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Dynamic WIFI Fingerprinting Indoor Positioning System
A technique is proposed to improve the accuracy of indoor positioning systems based on WIFI radio-frequency signals by using dynamic access points and fingerprints (DAFs). Moreover, an indoor position system that relies solely in DAFs is proposed. The walking pattern of indoor users is classified as dynamic or static for indoor positioning purposes. I demonstrate that the performance of a conventional indoor positioning system that uses static fingerprints can be enhanced by considering dynamic fingerprints and access points. The accuracy of the system is evaluated using four positioning algorithms and two random access point selection strategies. The system facilitates the location of people where there is no wireless local area network (WLAN) infrastructure deployed or where the WLAN infrastructure has been drastically affected, for example by natural disasters. The system can be used for search and rescue operations and for expanding the coverage of an indoor positioning system