Conference Proceeding Article
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 HMMbasedsequence 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, variable-order HMM-based sequence prediction, change point detection, feature-based landmarking combination, atomic item-level interactions, retail store, smartwatch, smartphone
Sales and Merchandising | Software Engineering
Software and Cyber-Physical Systems
2016 IEEE International Conference on Pervasive Computing and Communications PerCom: Sydney, March 14-19
City or Country
RADHAKRISHNAN, Meera; ESWARAN, Sharanya; MISRA, Archan; CHANDER, Deepthi; and DASGUPTA, Koustuv.
IRIS: Tapping wearable sensing to capture in-store retail insights on shoppers. (2016). 2016 IEEE International Conference on Pervasive Computing and Communications PerCom: Sydney, March 14-19. 7456526-1-8. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3238
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