We espouse a vision of small data-based immersive retail analytics, where a combination of sensor data, from personal wearable-devices and store-deployed sensors & IoT devices, is used to create real-time, individualized services for in-store shoppers. Key challenges include (a) appropriate joint mining of sensor & wearable data to capture a shopper's product-level interactions, and (b) judicious triggering of power-hungry wearable sensors (e.g., camera) to capture only relevant portions of a shopper's in-store activities. To explore the feasibility of our vision, we conducted experiments with 5 smartwatch-wearing users who interacted with objects placed on cupboard racks in our lab (to crudely mimic corresponding grocery store interactions). Initial results show significant promise: 94% accuracy in identifying an item-picking gesture, 85% accuracy in identifying the shelf-location from where the item was picked and 61% accuracy in identifying the exact item picked (via analysis of the smartwatch camera data).
['Meera Radhakrishnan', 'Sougata Sen', 'S Vigneshwaran', 'Archan Misra', 'Rajesh Krishna Balan']
IoT+Small Data: Transforming in-store shopping analytics & services