Sachen is so simple that anyone could use it.
Our RFIDs automatically communicate information about item identity and when it'll expire
Relax! Break up with your cumbersome labelling systems
The coordinate outputs of the computer vision system
Computer vision determines the coordinates of a person on a live feed
Set it and forget it. We'll do the rest.
Our friendly user interface lets you know the location of everything, all the time.
Our live website's home page! Hungry for more? Check out https://ayush4921.github.io/Sachen/
Inventory Management is Cumbersome
When's the last time you lost your keys? Had that rotten bell-pepper in the back of the fridge that you never got to?
What if we could make space-missions cleaner and more efficient? In space, keeping track of items such as tools, scientific equipment, medical supplies, personal belongings, food and more – is mission-critical. It's costly, damaging to the environment, and highly-complex to send up new items missed or lost during long-term space missions.
What do these problems have in common? Sachen solves both of them. We're the automatic inventory management system for everybody.
We Revolutionized Space-Grade Technology So That You Never Have to Think About Being Organized Again
Right now, NASA uses a series of antennas to track items on their space station, but these have a low location accuracy. The system is too complex, too expensive, and prone to breaking down, requiring complicated mathematical algorithms and heavy antennas every few feet. The extra weight of these antennas further raises the fuel needs and costs of space-flight.
So, we reinvented NASA's current, cumbersome inventory tracking system using RFID, Computer Vision, and AutoML AI to not only increases efficiencies, but save lives during deep-space missions. Our RFID scanner keeps track of when your items enter and leave your cabinets. Our Computer Vision algorithms let you know where you've placed them in the room. Our friendly, searchable, user interface lets you instantly find the location of any of your tracked items. Sachen is so simple, cost-effective, and fool-proof that anyone can use it. It even reminds you to eat your food when it's about to expire.
Sachen Makes the World Greener
Food waste is a worldwide epidemic. With Sachen, you'll be nudged when your food is about to expire. But no need to dig through your pantry! By telling you exactly where your expiring products are, we lower the barrier to you eating your food more efficiently.
The world would be cleaner if we never had to replace the things that we already have. Because you'll never lose your tracked items, you'll never have to purchase something that you've lost again, encouraging you to reuse your items as much as possible and eliminating consumer waste.
RFID, Python, Flask, and Google API: Seamlessly Integrated
Sachen works in two different ways.
We hook an RFID scanner up to all of your cabinets — or any other place that you want to keep track of. Think the coat rack where you always keep your purse, or the table where you leave your keys. Every item has a unique RFID identification code. When your item is within proximity to one of these RFID scanners, Sachen recognizes that you have put it there. C++, the language that Arduino communicates with, prints the cabinet's RFID reader value and Python reads reads it. We set a few second delay between scanning the same item with the same scanner so that we can accurately assess whether you've really taken the item out of the cabinet.
We built our database for tracking your items using Flask and Python. When you have a new item (an item with an unrecognized RFID code), simply put it near an RFID reader and it will automatically enter our database. Python communicates with Flask, which communicates with our website to keep you updated about where your items are in real time. The RFID code will confer information about expiration dates and item type, and so there's no need for you to edit the item once it reaches our servers. Of course, if you want, you can always come back to our friendly user interface later to edit your item attributes!
Forget putting RFID antennas every few feet. Sachen works with Google's Computer Vision API, so that you know where your things are when they're outside of your cupboards. Using machine learning, we sense what your object looks like and track it using a live computer feed. We then give you the coordinates of where your object is in your room in real-time, integrating with our Flask Application. The entire set-up requires only a couple of cameras, similar to a security system.
Making Sachen Smarter
Sounds great already? We want to make Sachen better. By training our computer vision algorithms to recognize more common household objects, we want to make Sachen the end-all be-all to tracking your stuff.
We will also create a mobile application so that you can get a notification when you put an item in the wrong place, or a warning when you're about to leave your house without your keys! Our mobile app will also suggest ways to combine your expiring items so that you don't have to think up a new recipe with that aging Gruyere, or that wilting Swiss Chard.
We also care about scaling Sachen up. Beyond making serial communication wireless, we want to create a system that relies solely on computer vision to scale up items. We'd use computer vision and cameras in each cabinet to get full coverage of where our things are. To avert privacy concerns, we could label our objects with distinctive bar codes, and encourage distributors to use these labels in their own inventory management systems. That way, everything you buy will be labelled automatically.
Have suggestions? Drop us a line!
So join the revolution. With Sachen, never forget again.
A Deep Learning Experience For Us All
Ayush, Charlotte, and Toby learned a lot through their experience with Sachen. Everyone came in with very different skillsets, and learned immensely from each other. Charlotte learned about front-end development, figured out how to use objects and dictionaries in more clever ways, and further improved upon her skills with interfacing Python and Arduino. Toby and Ayush who worked on processing the information coming from Charlotte and developing the computer vision inventory tracking system, learned how to use Pandas, Flask, OpenCV, Pagnation, and AutoML vision!