## What it does

Blink is a communication tool for those who cannot speak or move, while being significantly more affordable and accurate than current technologies on the market. [The ALS Association](http://www.alsa.org/als-care/augmentative-communication/communication-guide.html) recommends a $10,000 communication device to solve this problem—but Blink costs less than $20 to build.

You communicate using Blink through a modified version of **Morse code**. Blink out letters and characters to spell out words, and in real time from any device, your caretakers can see what you need. No complicated EEG pads or camera setup—just a small, unobtrusive sensor can be placed to read blinks!

The Blink service integrates with [GIPHY](https://giphy.com) for GIF search, [Earth Networks API](https://www.earthnetworks.com) for weather data, and [News API](https://newsapi.org) for news.

## Inspiration

Our inspiration for this project came from [a paper](http://www.wearabletechnologyinsights.com/articles/11443/powering-devices-through-blinking) published on an accurate method of detecting blinks, but it uses complicated, expensive, and less-accurate hardware like cameras—so we made our own **accurate, low-cost blink detector**.

## How we built it

The backend consists of the sensor and a Python server. We used a capacitive touch sensor on a custom 3D-printed mounting arm to detect blinks. This hardware interfaces with an Arduino, which sends the data to a Python/Flask backend, where the blink durations are converted to Morse code and then matched to English characters.

The frontend is written in React with [Next.js](https://github.com/zeit/next.js) and [`styled-components`](https://styled-components.com). In real time, it fetches data from the backend and renders the in-progress character and characters recorded. You can pull up this web app from multiple devices—like an iPad in the patient’s lap, and the caretaker’s phone. The page also displays weather, news, and GIFs for easy access.

**Live demo: [blink.now.sh](https://blink.now.sh)**

## Challenges we ran into

One of the biggest technical challenges building Blink was decoding blink durations into short and long blinks, then Morse code sequences, then standard characters. Without any libraries, we created our own real-time decoding process of Morse code from scratch.

Another challenge was physically mounting the sensor in a way that would be secure but easy to place. We settled on using a hat with our own 3D-printed mounting arm to hold the sensor. We iterated on several designs for the arm and methods for connecting the wires to the sensor (such as aluminum foil).

## Accomplishments that we're proud of

The main point of PennApps is to **build a better future**, and we are proud of the fact that we solved a real-world problem applicable to a lot of people who aren't able to communicate.

## What we learned

Through rapid prototyping, we learned to tackle difficult problems with new ways of thinking. We learned how to efficiently work in a group with limited resources and several moving parts (hardware, a backend server, a frontend website), and were able to get a working prototype ready quickly.

## What's next for Blink

In the future, we want to simplify the physical installation, streamline the hardware, and allow multiple users and login on the website. Instead of using an Arduino and breadboard, we want to create glasses that would provide a less obtrusive mounting method. In essence, we want to perfect the design so it can easily be used anywhere.

Thank you!
## Inspiration

About 0.2 - 2% of the population suffers from deaf-blindness and many of them do not have the necessary resources to afford accessible technology. This inspired us to build a low cost tactile, braille based system that can introduce accessibility into many new situations that was previously not possible.

## What it does

We use 6 servo motors controlled by Arduino that mimic braille style display by raising or lowering levers based upon what character to display. By doing this twice per second, even long sentences can be transmitted to the person. All the person needs to do is put their palm on the device. We believe this method is easier to learn and comprehend as well as way cheaper than refreshable braille displays which usually cost more than $5,000 on an average.

## How we built it

We use Arduino and to send commands, we use PySerial which is a Python Library. To simulate the reader, we have also build a smartbot with it that relays information to the device. For that we have used Google's Dialogflow.
We believe that the production cost of this MVP is less than $25 so this product is commercially viable too.

## Challenges we ran into

It was a huge challenge to get the ports working with Arduino. Even with the code right, pyserial was unable to send commands to Arduino. We later realized after long hours of struggle that the key to get it to work is to give some time to the port to open and initialize. So by adding a wait of two seconds and then sending the command, we finally got it to work.

## Accomplishments that we're proud of

This was our first hardware have to pulling something like that together a lot of fun!

## What we learned

There were a lot of things that were learnt including the Arduino port problem. We learnt a lot about hardware too and how serial ports function. We also learnt about pulses and how sending certain amount of pulses we are able to set the servo to a particular position.

## What's next for AddAbility

We plan to extend this to other businesses by promoting it. Many kiosks and ATMs can be integrated with this device at a very low cost and this would allow even more inclusion in the society. We also plan to reduce the prototype size by using smaller motors and using steppers to move the braille dots up and down. This is believed to further bring the cost down to around $15.
## Inspiration

The main problem we are focusing is the inability of differently abled people to communicate with normal people or other differently abled people. Now, we are here with the combined Hardware and Software solution which is called **“EME”**, after analysing some problems. And, try to make the next generation Support Device for differently abled people. (Deaf, mute & Blind)

## What it does

There is a Hardware project which consists of a Hand Gesture Recognition and Smart Blind Stick. Smart Blind Stick for “Blind People”, in this when object comes near to 1 m than less, then, buzzer beeping with increasing in beep frequency as object comes closer, through way they get an Alert.
Hand Gesture Recognition for “Dumb People (or mute people)”, because they have their own sign language but normal people don’t know the sign language which is used for intercommunication between mute people. This system will be useful to solve this problem and help them to make their interaction easily with the people. They can communicate hassle-free by using this device & with the help of our app EME, we converted gesture pattern into text to Speech.
In our app, there so many specifications-

1. Speech to Text (for Deaf people, to understand other people communication)
2. Text to Speech (for Dumb people with bluetooth connectivity)
3. Text to Speech (for Normally use)

## How we built it

In the Hardware of this project, Smart Blind Stick for “Blind People” using Adruino, Ultrasonic Sensor HC-SR04, IR Sensor & Buzzer to give alert and Hand Gesture Recognition for “Dumb People” using Arduino, Bluetooth HC 05, LDR Sensors & our app EME, through we convert gesture pattern into text to Speech.

We build our app on Flutter which is a hybrid platform using dart language. Because Flutter apps can run on ios and android devices. Then, Text to Speech with bluetooth connection and Speech to Text were implemented with our skills.

## Challenges we ran into

Due to lockdown, we don’t have much hardware components. So, we try to figure out this with Arduino Uno and Bluetooth in specific time duration. Also, make an app with such functioning is new to us.

## Accomplishments that we're proud of

Working of Hand Gesture recognition is so efficiently and accurately. Also, make a this device very cheap around 13 USD. ( There some sensors in market whose cost around 30 USD each then for four fingers 120 USD with other components additional cost also)
And, converted Text to Speech & Speech to Text successfully.

## What we learned

Learned a lot about that without having much components, how to make efficient project in hardware in specific time and also, learned about the new sensors.
Moreover, built a more functioning mobile app.

## What's next for EME

In terms of enhancing the project, we can introduce Obstacle alert Helmet for Blind People using circular rotate sensor like a radar and with the help of Gyrometer in hand gesture, we can increase the limits of signs means able for whole sentence instead of common words.
In terms of marketing the product, we would like to initially target differently abled people center of our region.