I⁴S: capturing shopper's in-store interactions

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 midsized 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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