Estimation of Fine-Grained Foot Strike Patterns withWearable Smartwatch De ices

People who exercise may benefit or be injured depending on their foot striking (FS) style. In this study, we propose an intelligent system that can recognize subtle differences in FS patterns while walking and running using measurements from a wearable smartwatch de ice. Although such patterns could be directly measured utilizing pressure distribution of feet while striking on the ground, we instead focused on analyzing hand mo ements by assuming that striking patterns consequently affect temporal mo ements of the whole body. The ad antage of the proposed approach is that FS patterns can be estimated in a portable and less in asi e manner. To this end, first, we de eloped a wearable system for measuring inertial mo ements of hands and then conducted an experiment where participants were asked to walk and run while wearing a smartwatch. Second, we trained and tested the captured multi ariate time series signals in super ised learning settings. The experimental results obtained demonstrated high and robust classification performances (weighted-a erage F1 score > 90%) when recent deep neural network models, such as 1D-CNN and GRUs, were employed. We conclude this study with a discussion of potential future work and applications that increase benefits while walking and running properly using the proposed approach.