DOI: 10.5281/zenodo.4292483
title: Data Visualization of Weight Sensor and Event Detection of Aifi Store

description: Aifi Store is an autonomus store for cashier-less shopping experience which is achieved by multi modal sensing (Vision modality, weight modality and location modality). Aifi Nano store layout (Fig 1) (Image Credits: AIM3S research paper). <strong>Overview:</strong><br> The store is organized in the gondola's and each gondola has shelfs that holds the products and each shelf has weight sensor plates. These weight sensor plates data is used to find the event trigger (pick up, put down or no event) from which we can find the weight of the product picked. Gondola is similar to vertical fixture consisting of horizontal shelfs in any normal store and in this case there are 5 to 6 shelfs in a Gondola. Every shelf again is composed of weight sensing plates, weight sensing modalities, there are around 12 plates on each shelf. Every plate has a sampling rate of **60Hz**, so there are 60 samples collected every second from each plate The pick up event on the plate can be observed and marked when the weight sensor reading decreases with time and increases with time when the put down event happens. <strong>Event Detection:</strong> The event is said to be detected if the moving variance calculated from the raw weight sensor reading exceeds a set threshold of (10000gm^2 or 0.01kg^2) over the sliding window length of 0.5 seconds, which is half of the sampling rate of sensors (i.e 1 second). There are 3 types of events: Pick Up Event (Fig 2)= Object being taken from the particular gondola and shelf from the customer Put Down Event (Fig 3)= Object being placed back from the customer on that particular gondola and shelf No Event = (Fig 4)No object being picked up from that shelf <strong>NOTE:</strong> 1.The python script must be in the same folder as of the <em>weight.csv</em> files and .<em>csv</em> files should not be placed in other subdirectories. 2.The videos for the corresponding weight sensor data can be found in the <strong>"Videos folder"</strong> in the repository and are named similar to their corresponding <strong>".csv"</strong> files. 3.Each video files consists of video data from 13 different camera angles. <strong>Details of the weight sensor files:</strong> These weight.csv (Baseline cases and team particular cases ) files are from the AIFI CPS IoT 2020 week.There are over 50 cases in total and each file has 5 columns (Fig 5) (timestamp, reading (in grams), gondola, shelf, plate number). Each of these files have data of around 2-5 minutes or 120 seconds in the form of timestamp. In order to unpack date and time from timestamp use <em>datetime</em> module from python. <strong>Details of the <em>product.csv</em> files:</strong> There are <em>product.csv</em> files for each test cases and these files provide the detailed information about the product name, product location (gondola number, shelf number and plate number) in the store, product weight(in grams), also link to the image of the product. <strong>Instruction to run the script:</strong> To start analysing the weigh.csv files using the python script and plot the timeseries plot for corresponding files. Download the dataset. Make sure to place the python/ jupyter notebook file is in same directory as the .csv files. Install the requirements<br> <code>$ pip3 install -r requirements.txt</code> Run the python script Plot.py<br> <code>$ python3 Plot.py</code> After the script has run successfully you will find the corresponding folders of weight.csv files which contain the figures (weight vs timestamp) in the format <strong>Instruction to run the Jupyter Notebook:</strong> Run the Plot.ipynb file using Jupyter Notebook by placing .csv files in the same directory as the Plot.ipynb script. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- gondola_number,shelf_number.png Ex: 1,1.png (Fig 4) (<em>Timeseries Graph</em>)
description: {"references": ["https://www.aifi.com/research", "Ruiz, Carlos & Falcao, Joao & Pan, Shijia & Noh, Hae & Zhang, Pei. (2019). AIM3S: Autonomous Inventory Monitoring through Multi-Modal Sensing for Cashier-Less Convenience Stores. 135-144. 10.1145/3360322.3360834."]}