Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
10-2018
Abstract
In this paper, we present I4S, a system that identifies item interactions of customers in a retail store through sensor data fusion from smartwatches, smartphones and distributed BLE beacons. To identify these interactions, I4S builds a gesture-triggered pipeline that (a) detects the occurrence of “item picks”, and (b) performs fine-grained localization of such pickup gestures. By analyzing data collected from 31 shoppers visiting a mid-sized stationary store, we show that we can identify person-independent picking gestures with a precision of over 88%, and identify the rack from where the pick occurred with 91%+ precision (for popular racks).
Keywords
Retail stores, Wearable computers, BLE beacons, Fine grained, Person-independent, Sensor data fusion
Discipline
Sales and Merchandising | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
ISWC '18: Proceedings of the 2018 ACM International Symposium on Wearable Computers, Singapore, October 8-12
First Page
156
Last Page
159
ISBN
9781450359672
Identifier
10.1145/3267242.3267259
Publisher
ACM
City or Country
New York
Citation
SEN, Sougata; MISRA, Archan; SUBBARAJU, Vigneshwaran; GROVER, Karan; RADHAKRISHNAN, Meeralakshmi; BALAN, Rajesh K.; and LEE, Youngki.
I4S: Capturing shopper’s in-store interactions. (2018). ISWC '18: Proceedings of the 2018 ACM International Symposium on Wearable Computers, Singapore, October 8-12. 156-159.
Available at: https://ink.library.smu.edu.sg/sis_research/4205
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3267242.3267259