Learn Sign Language Using Machine Vision

Learning a new language is a great way to exercise the mind and learn about different cultures, and it’s great to have a native speaker around to improve the learning experience. Without one it’s still possible to learn via videos, books, and software though. The task does get much more complicated when trying to learn a language that isn’t spoken, though, like American Sign Language. This project allows users to learn the ASL alphabet with the help of computer vision and some machine learning algorithms.

The build uses a computer vision model in MobileNetV2 which is trained for each sign in the ASL alphabet. A sign is shown to the user on a screen, and the user needs to demonstrate the sign to the computer in order to progress. To do this, OpenCV running on a Raspberry Pi with a PiCamera is used to analyze the frames of the user in real-time. The user is shown pictures of the correct sign, and is rewarded when the correct sign is made.

While this only works for alphabet signs in ASL currently, the team at the University of Glasgow that built this project is planning on expanding it to include other signs as well. We have seen other machines built to teach ASL in the past, like this one which relies on a specialized glove rather than computer vision.

Continue reading “Learn Sign Language Using Machine Vision”

Adding Brakes To Actuated Fingers

Building exoskeletons for people is a rapidly growing branch of robotics. Whether it’s improving the natural abilities of humans with added strength or helping those with disabilities, the field has plenty of room for new inventions for the augmentation of humans. One of the latest comes to us from a team out of the University of Chicago who recently demonstrated a method of adding brakes to a robotic glove which gives impressive digital control (PDF warning).

The robotic glove is known as DextrEMS but doesn’t actually move the fingers itself. That is handled by a series of electrodes on the forearm which stimulate the finger muscles using Electrical Muscle Stimulation (EMS), hence the name. The problem with EMS for manipulating fingers is that the precision isn’t that great and it tends to cause oscillations. That’s where the glove comes in: each finger includes a series of ratcheting mechanisms that act as brakes which can position the fingers precisely enough to make intelligible signs in sign language or even play a guitar or piano.

For anyone interested in robotics or exoskeletons, the white paper is worth a read. Adding this level of precision to an exoskeleton that manipulates something as small as the fingers opens up a brave new world of robotics, but if you’re looking for something that operates on the scale of an entire human body, take a look at this full-size strength-multiplying exoskeleton that can help you lift superhuman amounts of weight.

Continue reading “Adding Brakes To Actuated Fingers”

This Machine Teaches Sign Language

Sign language can like any language be difficult to learn if you’re not immersed in it, or at least learning from someone who is fluent. It’s not easy to know when you’re making minor mistakes or missing nuances. It’s a medium with its own unique issues when learning, so if you want to learn and don’t have access to someone who knows the language you might want to reach for the next best thing: a machine that can teach you.

This project comes from three of [Bruce Land]’s senior electrical and computer engineering students, [Alicia], [Raul], and [Kerry], as part of their final design class at Cornell University. Someone who wishes to learn the sign language alphabet slips on a glove outfitted with position sensors for each finger. A computer inside the device shows each letter’s proper sign on a screen, and then checks the sensors from the glove to ensure that the hand is in the proper position. Two letters include making a gesture as well, and the device is able to track this by use of a gyroscope and compass to ensure that the letter has been properly signed. It appears to only cover the alphabet and not a wider vocabulary, but as a proof of concept it is very effective.

The students show that it is entirely possible to learn the alphabet reliably using the machine as a teaching tool. This type of technology could be useful for other applications as well, such as gesture recognition for a human interface device. If you want to see more of these interesting and well-referenced senior design builds we’ve featured quite a few, from polygraph machines to a sonar system for a bicycle.

Continue reading “This Machine Teaches Sign Language”

3D Printed Robotic Arms For Sign Language

A team of students in Antwerp, Belgium are responsible for Project Aslan, which is exploring the feasibility of using 3D printed robotic arms for assisting with and translating sign language. The idea came from the fact that sign language translators are few and far between, and it’s a task that robots may be able to help with. In addition to translation, robots may be able to assist with teaching sign language as well.

The project set out to use 3D printing and other technology to explore whether low-cost robotic signing could be of any use. So far the team has an arm that can convert text into finger spelling and counting. It’s an interesting use for a robotic arm; signing is an application for which range of motion is important, but there is no real need to carry or move any payloads whatsoever.

Closeup of hand actuators and design. Click to enlarge.

A single articulated hand is a good proof of concept, and these early results show some promise and potential but there is still a long ways to go. Sign language involves more than just hands. It is performed using both hands, arms and shoulders, and incorporates motions and facial expressions. Also, the majority of sign language is not finger spelling (reserved primarily for proper names or specific nouns) but a robot hand that is able to finger spell is an important first step to everything else.

Future directions for the project include adding a second arm, adding expressiveness, and exploring the use of cameras for the teaching of new signs. The ability to teach different signs is important, because any project that aims to act as a translator or facilitator needs the ability to learn and update. There is a lot of diversity in sign languages across the world. For people unfamiliar with signing, it may come as a surprise that — for example — not only is American Sign Language (ASL) related to French sign language, but both are entirely different from British Sign Language (BSL). A video of the project is embedded below.

Continue reading “3D Printed Robotic Arms For Sign Language”

Speech To Sign Language

According to the World Federation of the Deaf, there are around 70 million people worldwide whose first language is some kind of sign language. In the US, ASL (American Sign Language) speakers number from five hundred thousand to two million. If you go to Google translate, though, there’s no option for sign language.

[Alex Foley] and friends decided to do something about that. They were attending McHack (a hackathon at McGill University) and decided to convert speech into sign language. They thought they were prepared, but it turns out they had to work a few things out on the fly. (Isn’t that always the case?) But in the end, they prevailed, as you can see in the video below.

Continue reading “Speech To Sign Language”

Hackaday Prize Entry: Hands|On Gloves Speaks Sign Language

The Hands|On glove looks like it’s a PowerGlove replacement, but it’s a lot more and a lot better. (Which is not to say that the Power Glove wasn’t cool. It was bad.) And it has to be — the task that it’s tackling isn’t playing stripped-down video games, but instead reading out loud the user’s sign-language gestures so that people who don’t understand sign can understand those who do.

The glove needs a lot of sensor data to accurately interpret the user’s gestures, and the Hands|On doesn’t disappoint. Multiple flex sensors are attached to each finger, so that the glove can tell which joints are bent. Some fingers have capacitive touch pads on them so that the glove can know when two fingers are touching each other, which is important in the US sign alphabet. Finally, the glove has a nine degree-of-freedom inertial measurement unit (IMU) so that it can keep track of pitch, yaw, and roll as well as the hand’s orientation.

In short, the glove takes in a lot of data. This data is cleaned up and analyzed in a Teensy 3.2 board, and sent off over Bluetooth to its final destination. There’s a lot of work done (and some still to be done) on the software side as well. Have a read through the project’s report (PDF) if you’re interested in support vector machines for sign classification.

Sign language is most deaf folks’ native language, and it’s a shame that the hearing community can’t understand it directly. Breaking down that barrier is a great idea, and it makes a great entry in the Hackaday Prize!

ASL Glove

Electronic Glove Detects Sign Language

A team of Cornell students recently built a prototype electronic glove that can detect sign language and speak the characters out loud. The glove is designed to work with a variety of hand sizes, but currently only fits on the right hand.

The glove uses several different sensors to detect hand motion and position. Perhaps the most obvious are the flex sensors that cover each finger. These sensors can detect how each finger is bent by changing the resistance according to the degree of the bend. The glove also contains an MPU-6050 3-axis accelerometer and gyroscope. This sensor can detect the hand’s orientation as well as rotational movement.

While the more high-tech sensors are used to detect most characters, there are a few letters that are similar enough to trick the system. Specifically, they had trouble with the letters R, U, and V. To get around this, the students strategically placed copper tape in several locations on the fingers. When two pieces of tape come together, it closes a circuit and acts as a momentary switch.

The sensor data is collected by an ATmega1284p microcontroller and is then compiled into a packet. This packet gets sent to a PC which then does the heavy processing. The system uses a machine learning algorithm. The user can train the it by gesturing for each letter of the alphabet multiple times. The system will collect all of this data and store it into a data set that can then be used for detection.

This is a great project to take on. If you need more inspiration there’s a lot to be found, including another Cornell project that speaks the letters you sign, as well as this one which straps all needed parts to your forearm.
Continue reading “Electronic Glove Detects Sign Language”