Publication Type

Conference Proceeding Article

Publication Date

3-2016

Abstract

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%.

Keywords

IRIS, variable-order HMM-based sequence prediction, change point detection, feature-based landmarking combination, atomic item-level interactions, retail store, smartwatch, smartphone

Discipline

Sales and Merchandising | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2016 IEEE International Conference on Pervasive Computing and Communications PerCom: Australia, Sydney, 14-19 March 2016

ISBN

9781467387781

Identifier

10.1109/PERCOM.2016.7456526

Publisher

IEEE

City or Country

Piscataway, NJ

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1109/PERCOM.2016.7456526

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