Konings DAlam FFaulkner Nde Jong C2022-09-232022-09-2023/09/2022Sensors (Basel), 2022, 22 (19)https://hdl.handle.net/10179/17759In 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.biometricscapacitive floorgender classificationhuman sensingmachine learningneural networkAlgorithmsHumansMachine LearningNeural Networks, ComputerSupport Vector MachineJournal article10.3390/s221972064569171424-8220Massey_Dark0301 Analytical Chemistry0805 Distributed Computing0906 Electrical and Electronic Engineering0502 Environmental Science and Management0602 Ecology