IoT+Small Data: Transforming In-Store Shopping Analytics and Services

• Main Authors: RADHAKRISHNAN, Meera, SEN, Sougata, SUBBARAJU, Vigneshwaran, MISRA, Archan, BALAN, Rajesh
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Accelerometers; Cameras; Object recognition; Image recognition; Performance evaluation; Real-time systems; Data mining; Computer and Systems Architecture; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/3570; https://ink.library.smu.edu.sg/context/sis_research/article/4571/viewcontent/1570228275.pdf
• Institution: Singapore Management University

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).