I4S: Capturing shopper’s in-store interactions

I4S: Capturing shopper’s in-store interactions

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 “...

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Bibliographic Details
Main Authors: SEN, Sougata, MISRA, Archan, SUBBARAJU, Vigneshwaran, GROVER, Karan, RADHAKRISHNAN, Meeralakshmi, BALAN, Rajesh K., LEE, Youngki
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2018
Subjects:
Retail stores
Wearable computers
BLE beacons
Fine grained
Person-independent
Sensor data fusion
Sales and Merchandising
Software Engineering
Online Access:https://ink.library.smu.edu.sg/sis_research/4205
https://ink.library.smu.edu.sg/context/sis_research/article/5208/viewcontent/I4S_sen_2018.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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).

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