I4S: Capturing shopper’s in-store interactions

• 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
• Institution: Singapore Management University

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).