A smart table mat that will efficiently manage a restaurant.
Each table mat can detect when a guest has finished his/her meal and summon waiters.
The mat uses light and weight sensors to detect this.
Each table mat detects and logs the amount of food left over on a plate. This data from table mats can be used to calculate the most/least popular dishes and food wastage
The number of active table mats can be used to determine the number of people at a restaurant.
This information can ued to provide customers with real-time table-availability at a restaurant.
Today, restaurants struggle to automate waiting of tables, have trouble knowing
whether customers were satisfied with the dishes, and are unable to
identify items from the menu that customers do not finish.
From the public perspective, avoiding waiting lines at restaurants still remains a challenge
because we cannot find out in real-time how packed a restaurant is.
Therefore, a smart table mat will help
restaurants automate prompt service, waste management and crowd control.
Alternative methods to gather customer data/automate table-waiting
Baidu and KFC have partnered to use facial recognition to gather data about
their customers such as age, gender and expression and recommend dishes.
This does keep track of customers but only recommends based on past orders
and does not detect accurately. It also doesn't work for one-time customers.
Many restaurants such as Eatsa in San Francisco and some restaurants in
the New York airport let customers order food using tablets. They
reduce the wait and ordering time but are extremely expensive and do
not provide food-wastage analytics.
Detects the presence of a customer and hence the headcount in the restaurant
Calibrates the plate for accurate food wastage statistics (as plates differ from restaurant to restaurant)
Detect when customer is close to finishing a course and alert the waiters
Measure weight of food left over from each course
Poll every 20 seconds to detect if a plate has arrived. Until plate arrives, put itself (atmega) and the HM-10 to sleep to save power.
When a plate is detected:
Allows the plate to reach a steady-state
Poll ADC values of the FSRs every 5 seconds
The HM-10 will read these values
When the plate is removed go back to the first step
Low Power Communication
Transmit FSR values using BLE to an aggregator which keeps track of meal sessions
Keeps track of state and meal sessions
Chooses when to send information to the server (meal in progress) and when to wait for a session (customer not arrived)
Receives raw values over bluetooth and sends them over wifi to the server
Analytics for the Restaurant
Number of dishes ordered at different times of the day
Best and worst dishes for different meal times
Shortest and longest times for serving and consuming dishes
Food that is wasted the most and least
Menu items that bring in the most and least revenue
The most and least crowded times
Analytics for the Customer
Seat-availability in a restaurant based on table size
Relative popularity of dishes at a particular restaurant
Finding restaurants based on cuisine and price
Durable and Spill-Proof: Table-mat should not be repaired if customer spills food or beverages onto it
Non-obstructive: Nothing obstructing the area where the cutlery is kept
Real-time: customer-facing web application should update information by the minute
Reliable: Table-mat should not call waiters at the wrong time and not fail while customer is dining
Failure resiliency: In case of our components running out of power and not accepting connections our aggregator should take care of this and not crash.
Accurate: Server should maintain state from course to course in order to reflect real time analysis and report accurate weight readings
Easy Installation: Table-mats must be able to be installed and maintained without much effort (Easy power connections/long-lasting batteries to prevent changing frequently)
Automate waiting in a busy restaurant: Waiters do not have to keep an eye out for signalling customers
Identify sources of food wastage: Certain dishes are always thrown out and are not profitable anymore
Identify sources of revenue: Certain dishes are crowd favorites and are always ordered
Menu optimization: Based on the above, restaurants can customize their menu with high revenue, popular items
Crowd control and monitoring: Have information on how many consumers visited per meal time/per day/per month
Automate waiting in a busy restaurant: Customers do not have to signal waiters
Real time table availability: No need to ever wait it line for a table again and waste time
Menu information: If you are new to the restuarant, you can check what the most popular dishes are currently
You want a particular cuisine: You now know the cheapest/nearest/most popular restaurant to get it
You want a particular dish: You now know the best/cheapest restaurant to get it
I think our project has potential to grow as it is easy to use and install and can provide a ton of beneficial analytical information. I love building things and designing PCBs!
Two fun facts about me:
Unofficial ambassador of Mountain Dew
I want to work with embedded systems to help humanity
I once built a Daft Punk helmet. Currently interested in GPU computing and building embedded devices.
I like to eat good food, train for marathons and hike. I also love to code in C and java.