DiSen: Ranging Indoor Casual Walks with Smartphones

Abstract

Acquiring instant walking distance is desirable in indoor localization and map construction. However, due to the blackout of Global Positioning System (GPS) in indoor settings, to accurately estimate the indoor walking distance with minimum hardware requirement is very challenging. In this paper, we propose a lightweight scheme, called DiSen, to range the instant walking distance of smartphone users. After analysing the extensive walking trace data, we find that people have rather consistent walking behaviour even though they may change their walking speeds in different situations. Furthermore, the relationship between stride length and step frequency while walking can be well estimated using non-linear sigmoid model. Inspired by such insights, we first design a stride segmenting method to obtain reliable and accurate step frequency information from raw accelerometer readings. We then train a sigmoid model using acceleration and GPS information collected when a user walks in outdoor conditions and finally apply the model to indoor walking distance ranging. Real-world experiment results show that, in different walking speeds, DiSen can reach average distance estimation accuracy of 96%.