Temporal Pattern Recognition in Gait Activities Recorded With a Footprint Imaging Sensor System

In this paper, we assess the capability of a unique unobtrusive footprint imaging sensor system, based on plastic optical fiber technology, to allow efficient gait analysis from time domain sensor data by pattern recognition techniques. Trial gait classification experiments are executed as ten manners of walking, affecting the amplitude and frequency characteristics of the temporal signals. The data analysis involves the design of five temporal features, subsequently analyzed in 14 different machine learning models, representing linear, non-linear, ensemble, and deep learning models. The model performance is presented as cross-validated accuracy scores for the best model-feature combinations, along with the optimal hyper-parameters for each of them. The best classification performance was observed for a random forest model with the adjacent mean feature, yielding a mean validation score of 90.84% ± 2.46%. We conclude that the floor sensor system is capable of detecting changes in gait by means of pattern recognition techniques applied in the time domain. This suggests that the footprint imaging sensor system is suitable for gait analysis applications ranging from healthcare to security.