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Abstract-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 clients footstep data and provide insights of the feature learning process.

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We investigated cognitively demanding tasks on patterns of human gait in healthy adults with a deep learning methodology that learns from raw gait data. Agerelated differences were analyzed in dual-tasks in a cohort of 69 cognitively healthy adults organized in stratified groups by age. A novel spatio-temporal deep learning methodology was introduced to effectively classify dualtasks from spatio-temporal raw gait data, obtained from a tomography floor sensor. The approach outperformed traditional machine learning approaches. The most favorable classification F-score obtained was of 97.3% in dual-tasks in a young age group experiment for a total of 12 users. The deep machine learning methodology outperformed classical machine learning methodologies by 63.5% in the most favorable case. Finally, a 2D manifold representation was obtained from trained deep learning models' data, to visualize and identify clusters from features learned by the deep learning models. This study demonstrates a novel approach to dual-task research by proposing a data-driven learning methodology with stratified age-groups.

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