Everyday, billions of people use footwear for walking, running, or exercise. Of emerging interest are ``smart footwear'', which help users track gait, count steps or even analyse performance. However, such nascent footwear lack fine-grain ground surface context awareness, which could allow them to adapt to the conditions and create usable functions and experiences. Hence, this research aims to recognize the walking surface using a radar sensor embedded in a shoe, enabling ground context-awareness. Using data collected from 23 participants from an in-the-wild setting, we developed several classification models. We show that our model can detect five common terrain types with an accuracy of 80.0\% and further ten terrain types with an accuracy of 66.3\%, while moving. Importantly, it can detect the gait motion types such as `walking', `stepping up', `stepping down', `still', with an accuracy of 90\%. Finally, we present potential use cases and insights for future work based on such ground-aware smart shoes.
https://doi.org/10.1145/3586183.3606738
ACM Symposium on User Interface Software and Technology