Resistance in Motion: What You Should Know About Variable Resistors

Adjusting the volume dial on a sound system, sensing your finger position on a touch screen, and knowing when someone’s in the car are just a few examples of where you encounter variable resistors in everyday life. The ability to change resistance means the ability to interact, and that’s why variable resistance devices are found in so many things.

The principles are the same, but there are so many ways to split a volt. Let’s take a look at what goes into rotary pots, rheostats, membrane potentiometers, resistive touchscreens, force sensitive resistors, as well as flex and stretch sensors.

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Chewing Gum Plus Carbon Nanotubes

Normally, strain sensors are limited in their flexibility by the underlying substrate. This lead researchers at the University of Manitoba to an off-the-wall solution: mixing carbon nanotubes into a chewing-gum base. You can watch their demo video below the break.

The procedure, documented with good scientific rigor, is to have a graduate student chew a couple sticks of Doublemint for half an hour, and then wash the gum in ethanol and dry it out overnight. Carbon nanotubes are then added, and the gum is repeatedly stretched and folded, like you would with pizza dough, to align the ‘tubes. After that, just hook up electrodes and measure the resistance as you bend it.

The obvious advantage of a gum sensor is that it’s slightly sticky and very stretchy. The team says it works when stretched up to five times its resting length. Try that with your Power Glove.

We’ve seen a couple different DIY flex sensor solutions around these parts, one based on compressing black conductive foam and another using anti-static bags, but the high-tech, low-tech mixture of nanotubes and Wrigley’s is a new one.

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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.
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Flex Sensing for a DIY Data Glove

cyber

[Cyber] has been testing out intuitive input methods for virtual reality experiences that immerse the user further into the virtual world than archaic devices like a keyboard or mouse would allow. One of his biggest interests so far was the idea of a data glove that interacts with an Arduino Uno to interface with a PC. Since commercial products are yet to exist on a readily available level, [Cyber] decided to build his own.

He started out with a tiny inertial measurement unit called a Pololu MinIMU-9 v2 that tracks orientation of the 3-axis gyro and accelerometer. The USB interface was soldered into place connecting the wires to an Arduino Uno. From there, he hooked up a flex sensor from Spectra Symbol (which were supposedly used in the original Nintendo Power Gloves) and demoed the project by tracking the movement of one of his fingers. As the finger bent, the output printed on the serial monitor changed.

[Cyber] still needs to mount a glove on this system and construct a proper positional tracking method so that physical movement will be mirrored in a simulation.

[Cyber’s] day job has had him busy these last few months, which has forced the project into a temporary hold. Recently though, [Cyber] has been an active member and an influence in the local Orange County VR scene helping to build a nice development culture, so we’re hoping to see more updates from him soon.

To view what he has done up to this point, click the link at the top of the page, and check out the video after the break:

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Making flex sensors on the cheap

When [Michelle] was making a sign language translation glove, she needed a bunch of flex sensors. These flex sensors cost about $10 a pop, meaning her budget for the project was eaten up by these bendy potentiometers. Since then, [Michelle] figured out a great way to make extremely inexpensive bend sensors using anti-static bags and masking tape, allowing her to start her project once again.

The build works by sandwiching Velostat plastic bags – the same electrically conductive bags all your components arrive in – between layers of masking tape. A jumper wires is attached to a strip of Velostat attached to a piece of masking tape. Between two of these anti-static/masking tape assemblies, another piece of Velostat is placed. After laminating all these pieces together, [Michelle] had a primitive yet very functional flexible potentiometer.

After attaching one of these flex sensors to an analog input of her dev board of choice, she had a wonderful and inexpensive flexible sensor. You can check out this sensor in action after the break.

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Failed kayaking data logger is something we want to see succeed

How can your love of hobby electronics and your participation in the Canadian National Kayaking Team be combined? Why not use your technical know-how to provide a performance edge? [Geoff Clarke] decided to rig up a paddle for data capture to see if they could learn anything.

Here you can see that a series of flex sensors were applied to one of the business ends of the paddle. These are connected to a microcontroller which is constantly monitoring them and dumping the data onto an SD card. The design will provide about nine minutes of data before the storage is used up. That sounds like a number that might need improving. We could see this being useful to log a series of practice runs on the same course, but with different athletes. By graphing and comparing the data, you should be able to make observations about how the paddle is being held and when force is applied that could help the rest of the team improve.

But we’re way ahead of ourselves. The rig was given a premature test-run and the flex sensors were destroyed by the salt water. We wish this had worked out and hope that [Geoff] will give it another try after rethinking the water proofing.

Sign and speak glove

This wire covered glove is capable of turning your hand gestures to speech, and it does so wirelessly. The wide range of sensors include nine flex sensors, four contact sensors, and an accelerometer. The flex sensors do most of the work, monitoring the alignment of the wearer’s finger joints. The contact sensors augment the flex sensor data, helping to differentiate between letters that have similar finger positions. The accelerometer is responsible for decoding movements that go along with the hand positions. They combine to detect all of the letters in the American Sign Language alphabet.

An ATmega644 monitors all of the sensors, and pushes data out through a wireless transmitter. MATLAB is responsible for collecting the data which is coming in over the wireless link. It saves it for later analysis using a Java program. Once the motions have been decoded into letters, they are assembled into sentences and fed into a text-to-speech program.

You’ve probably already guess that there’s a demo video after the break.

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