2016 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2016)
March 14, 2016 to March 19, 2016
Meera Radhakrishnan , School of Information Systems, Singapore Management University
Sharanya Eswaran , Xerox Research Center India
Archan Misra , School of Information Systems, Singapore Management University
Deepthi Chander , Xerox Research Center India
Koustuv Dasgupta , Xerox Research Center India
We investigate the possibility of using a combination of a smartphone and a smartwatch, carried by a shopper, to get insights into the shopper's behavior inside a retail store. The proposed IRIS framework uses standard locomotive and gestural micro-activities as building blocks to define novel composite features that help classify different facets of a shopper's interaction/experience with individual items, as well as attributes of the overall shopping episode or the store. Besides defining such novel features, IRIS builds a novel segmentation algorithm, which partitions the duration of an entire shopping episode into atomic item-level interactions, by using a combination of feature-based landmarking, change point detection and variable-order HMM-based sequence prediction. Experiments with 50 real-life grocery shopping episodes, collected from 25 shoppers, we show that IRIS can demarcate item-level interactions with an accuracy of approx. 91%, and subsequently characterize item-and-episode level shopper behavior with accuracies of over 90%.
Iris, Iris recognition, Sensors, IEEE 802.11 Standard, Biomedical monitoring, Feature extraction, Tracking
M. Radhakrishnan, S. Eswaran, A. Misra, D. Chander and K. Dasgupta, "IRIS: Tapping wearable sensing to capture in-store retail insights on shoppers," 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)(PERCOM), Sydney, Australia, 2016, pp. 1-8.