Many people around the world suffer from speech problems, and can't communicate with others in the same way we do it. To solve that problem, I, Bruno Moraes, and my research partner, Pedro Jorge, developed a Sign Language Translation Glove. Acctually, it only works with Brazillian Sign Language.
The Glove is also able to control a LEGO Wheelchair wirelessly with the movement of your fingers.
We're both 15, we don't know that much about electronics and programming, so go easy.
For this project, we made use of flexible potentiometers (available at sparkfun) to measure each finger's position. This potentiometer, together with a 10K resistor form a Voltage Divider, that feeds de Arduino ADC. We used an Arduino Mega 2560, but UNO or similars is ok.
Bill of Materials:
1 - Glove ( We used a Nike Dry Fit Tour Golf )
1 - Arduino Mega 2560 (http://www.sparkfun.com/products/9949)
1 Arduino UNO ( Optional, used for the wheelchair side control)
5 - Flexible potentiometers (http://www.sparkfun.com/products/8606)
5 - 10K Resistors
2 - 2.2K Resistors (Optional, used for the wheelchair side control)
2 - 2N2222 Transistors (Optional, used for the wheelchair side control)
1 - Breadboard
2 - XBee Series 1 Modules
2 - XBee Explorer Regulated
1 - Soldering Iron
1 - Solder
1 - Stranded wire
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Signing UpStep 1: Attach the Sensors
After, solder 15cm stranded wire pieces to each of the sensor leads.
Remember to reinforce de pin-end of the sensor, this area is susceptible to kinking and eventual failure. Electrical tape ou heatshrink is fine.






































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Estamos testando aí o que o maewert sugeriu, mas tá engatinhando ainda. Pra programar estamos usando a IDE do Arduino mesmo, até agora atende todas as nossas necessidades.
Abraços,
If you wanted to read American Sign Language (and I assume Brizilian as well) you would need to record every joint position from the shoulder to the finger tips and to read and understand their motions. Reading the alphabet is a much more modest project as most letters in the american sign language do not require the decoding of motion (that dang Z requires motion).
I am sure you understand full well the problems with reading characters (but I'll discuss them briefly anyway!) When a person is signing no finger position readings for a character is exactly the same each time the character is signed. Not only is there noise in the glove's electronics but the signer also changes the fingering slightly based on the characters which are signed before and after. Human sign language readers actually use super computers (i.e. their brains) to decode these subtile differences and use them to reinforce their understanding of the signs. (The same occurs in people with full hearing - they actually hear and read lips subconsciously as well and it is shown that people 'hear' better when they are presented with sound and lip movement than with either sound or lip movement alone.) These additional queues are actually needed to help people reduce their read error rates but increase the read error rates for the computer since the signs are subtilely different for the same character.
Taking this concept one step further, the human reader has the benefit of knowing that the characters make up words and mostly anticipates the characters they read. In english for example the 'q' is almost always followed by 'u'. Many words have standard prefixes and suffixes as well which help us understand the signs. For example if the letter 'E' was close to the letter 'F' the reader would read 'NATIVE' and not 'NATIVF' without a thought - in fact it would be hard to get them to actually read 'NATIVF' if that is what you really meant! The reader also have the advantage over the computer in that the reader understands the contect of the discussion and uses that expectation of possible words to improve read error rates. Your read error rates are very reasonable and you really haven't yet employed these other improvements. Good Job.
One of the methods I had considered using was a Binary Associative Memory. A BAM is really a manipulation of matricies which can be used to return one of a set of a limited number of trained values. This is how it works: You take the measurements of the finger positions when someone signs each 'perfect' letter. The BAM is then trained using these 'perfect' letters. After the training, when someone signs one of these letters, the measurements read in will be a little different but close to the 'perfect' letter positions. You can apply these slightly different positions to the BAM and it will return the letter with the closest match to the 'prefect' letters. The advantages of using a BAM is that the BAM is easy to code and does not care about the patterns being learned, so it could be used to 'learn' to read american sign language as easily as any other sign language. BAMs always return the closest matching pattern, however, even if the patterns don't match very closely at all, so if you use a BAM you'd need filter out the patterns that just don't match very closely.
Best Wishes!
Also, and bear in mind I do not have much knowledge of sign language, doesn't it often use both hands for certain words and phrases? perhaps if as you suggest arm motion is brought into the equation it would also be good to make it a pair of gloves as opposed to the single golf glove that it currently is.
all that aside this project is amazing, and I wish you the best of luck brunomoraes.
Another method might be to place a band of different colors on each finger tip and use a video camera that can then track the positions of each color. This is beyond me :-) but might be a good solution as well.
Best Wishes to brunomoraes
maewert, your idea about using BAM was really helpful, I started writing some test code yesterday. That's our second electronics project, so we're not looking far beyond letters right now. In one of our early meetings, the camera tracking idea came up, but we're not familiarized at all with this.
We have an accelerometer, the main idea was to include it as soon as we could, but we decided to buy an IMU, that will give us more options for extending this project beyond Sign Language Translation, we're waiting it to arrive.
We'll also attending the FIRST Lego League in Dec. 10th in our city with this project. We took the earlier prototype of this concept back in April, in the FIRST Lego League World Championship. The first idea used QR Codes together with Augmentative and Alternative Communication (CAA) to interact with deficient kids in our school.
Sorry for not including the code yet, we didn't have time to work on the project this weekend, and all the code is in the lab computer.
Thanks for your help lavothas and maewert!