Colorful PDR: Colorizing PDR with shopping context in walking

Abstract

The technology for detection of indoor location information has been attracting much attention in the recent past, since such information can be utilized for navigation and marketing research. The commonly used indoor location detection methods are Wi-Fi positioning, Bluetooth Low Energy (BLE) beacons and Pedestrian Dead-reckoning (PDR). PDR can provide location information relative to the original position by analyzing acceleration and angular velocity using sensors in smartphones without needing any positioning equipment in the environment. Meanwhile, to know the user's context in the indoor environment, PDR, RFID and surveillance cameras are investigated. Although PDR results can provide the atomic physical behavior such as the direction of walking (forward/backward or right/left), it is difficult to detect the user's shopping behaviors to determine if he/she is approaching, searching or just browsing in the shopping venues. Our research objective is to estimate the user's walking context appropriate for shopping venues with the same sensor information as used for conventional PDR. We propose a new method for walking context recognition that uses machine learning with a feature vector of accelerometers and gyroscopes. Based on the experiments conducted, we found that this model was able to recognize the intuitive walking context. We also established the utility of our proposal in the experiment at the actual shopping venue and verified that it can be applied to improve PDR positioning precision by 8%.