Remote-Controlled Bluetooth-Enabled Smart Shopping Cart: Prototype and Evaluation
Bibliographic record
Abstract
In an endeavor to enhance consumer experience and streamline the shopping process, this study presents the design and evaluation of a smart shopping cart prototype, devised to replace conventional manual shopping carts.Customers traditionally utilize baskets to transport purchased goods to the cashier for payment processing.During peak shopping hours, queues often form, potentially detracting from the customer's overall shopping experience.Customers are typically obliged to stand adjacent to their baskets to maintain their position in the queue, a practice this innovation intends to eliminate.The proposed smart shopping cart employs Bluetooth HC-05 technology to facilitate remote operation, thereby enabling users to wait at a comfortable distance from their groceries.The remote control for the cart is designed to interface with Android smartphones, capitalizing on the ubiquity of these devices in customers' daily lives.The ease of connecting Bluetooth HC-05 with an Android device further simplifies the operation for users well-versed with smartphones.This study outlines the construction and testing of a smart shopping cart prototype, although it has not been directly implemented in a supermarket setting.The conducted tests are representative of the performance of a potential full-scale model.The remote control application, tailored to the specific commands required by the shopping cart, was developed using Kodular.It can be easily accessed and downloaded from the Kodular website.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".