YouTube / Jonathan Dyer – via Iframely

Our team got the idea for Repman because one of our team members is a collegiate athlete and because of that, even though he has been lifting for years, any time he has lifted it’s always been with a trainer. Until just recently, when he had a peculiar experience – lifting while keeping his own keeping sets, reps and most importantly tempo all on his own. Any experienced lifter understands that tempo is very important in the weight room and it is often hard to keep on your own. So our group set out to correct this problem by creating Repman, the Perfect Workout Companion.

Repman counts your reps as you go and vibrates when you have completed your set. It then waits for a set rest period, and vibrates three times when it is time to start the next set. At this point, the user can get into position and tap the wristband to start the next set. Repman works on multiple workouts, including benchpress, curls, and even shoulder press. It can be used throughout your entire workout without distracting from it, since it doesn’t require any outside devices like phones.

For our first design we used a store bought wristband, made a pressure sensor to be used as a button and housed the Light Blue Bean on the top of the wrist. The idea from our initial design came from talking to other members of Carnegie Mellon’s football team and researching existing designs for similar wearables. The picture shown is of a wristband that has the same functions as our wristband (and more) called the “push” band (http://www.trainwithpush.com/). Our Wrist Band accomplished the same design with a 3D printed casing and would attach by sewing it into the wristband.

After being told by our teachers that people would likely prefer to simply tap the wristband while working out to use it, we researched CapSense buttons and how to use them. CapSense added several complications that prohibited us from developing a more through design for the casing of the band. We were also told to expand our sensing to several other workouts by making our peak/trough detection algorithm more robust which we were able to do and the improved code is below. We also added the ability to keep track of rest periods, buzz when the rest period is over and start the next set. For our next iteration of the project, we plan to create a simple phone app that will allow the user to input their own sets and reps, refine the casing and improve our peak detecting algorithm even more.

Repman uses a Light Blue Bean microcontroller, conductive fabric (for a Capsense button) and a vibe motor. The schematic of the circuit only requires a few pins of the Light Blue Bean. A vibe motor is connected between Pin 3 and a ground pin. Pin 4 is connected to conductive fabric on the outside of the wristband, which acts as a button. The Arduino CapSense library is used to sense taps on the button. Lastly, the ground pin needs to be grounded, so it pin is connected to conductive fabric on the inside of the wristband. This makes it so that the person wearing the wristband touches the conductive fabric and acts as the ground for the capacitive touch sensing. A detailed picture of the circuit digram can be found below:

In this code, we wait until the start button has been pressed. After the start button has been pressed, we start looking for reps. Each time a rep has been completed, we increment a rep counter variable by one. Rep detection works by taking acceleration data and detecting if there has been a peak or valley. This is motivated by the fact that doing a rep entails moving forward and backwards along a path. Thus, somewhere along that path, acceleration must reverse. The code looks for points where the acceleration changes direction and counts that as one rep. After ten reps have been completed, Repman will buzz, and then sleep for 30 seconds (standard rest period for athletes). All variables related to rep detection will be reset. After 30 seconds, Repman will start waiting for the start button to be pressed, restarting the process. A Github link to the code can be found below:

When you hear the word “wearable”, what’s the first company you think of? Most likely your answer to the question is “Fitbit”, the $300 million dollar wearable company that just filed for an IPO. Fitbit has made their money by promising to improve your workout by tracking your heart rate and telling you how effective you’ve been. Though, how useful is information that someone would have to teach you how to use? Wearables should compliment and improve our current workout routines, not force us to adopt an entirely new behavior. That was our thinking when we designed “Repman”, a rep tracking wristband.

Repman is a wristband that uses accelerometers to count your reps as you go and vibrates when you’ve finished your set. There is no need to interact with an app or apple watch, just simply press the button and begin your workout.

The wristband works by collecting Y acceleration data from the Light Blue Bean. It first smooths the data using a smoothing filter found on the Arduino website. It then uses an algorithm to detect peaks in the data and counts each peak as a rep. It discards any rep that is too close too another, interpreting it as noise. The code and circuit diagram can be found here:

YouTube / Jonathan Dyer – via Iframely

For our first prototype we built a really cool light following robot. It uses photo resistors to sense how far it is from the light and moves to keep that distance. But once we started to think about how our robot would interact with other robots we realized that our robot is kind of obsessive about light, like what if another robot were just using light to find its way home… this robot would basically be a stalker.

This was really interesting to us so we programmed another robot to help us play out this scenario. we call the light following robot “stalker” and the light emitting robot “prey”. The prey robot is equipped with lights to simulate it trying to find its way home. It is also equipped with a distance sensor so that when it realizes its being followed it turns its lights out & ” freaks out” by spinning around. Once the light goes out, the stalker bot spins around as well and acts as if it were never following the other robot.  Once the “prey” bot calms down and turns the light back on, the stalker bot resumes its chase.

On the stalker bot, light detection is accomplished through two voltage divider circuits with photoresistors attached to two of the 3pi’s analog-to-digital-conversion pins. On the prey bot, the ultrasonic distance sensor uses two of the 3pi’s digital I/O pins to generate pulses (TRIG) and read in the pulses that result (ECHO). The prey bot’s two LEDs are wired in parallel to a third digital I/O pin.

For those interested in creating their own “stalker” and “prey” robots or maybe just using their behavior in other projects, the github links to the C++ code for the 3pi bots can be found below:

Prey Code
Stalker Code

Here are circuit diagrams for each robot if you’d like to wire them yourself:

Prey:

Stalker:

For our final presentation, we decided to take the stalker and prey metaphor a little further by adding a social commentary about privacy in the age of social media. We attached an action figure like picture of Mark Zuckerberg to the prey bot and Edward Snowden to the stalker bot. The interaction between the two robots now symbolizes the relationship between Mark Zuckerber and Edward Snoden before Snowden revealed that he’d been part of an NSA project that used a fake Facebook server to intercept people’s information.

The most interesting part about this addition to the project was that once the robots were allowed to interact long enough, one quickly began to lose track of which robot was stalking and which was the prey. This observation elicited conversation about Snowden’s current role as a sort of vigilante exposing U.S. secrets about privacy infringements.

The main function of SSL lies within the two half circles of conductive tubing surrounding the octagonal case. The tubing has a linear resistance, which allows the user to create different resistances by grounding the tube at different locations. The Arduino reads the resistance and calculates the position of the ring along the tube. The Arduino then sends the position to a Macbook hidden under the case over a serial connection, using the Firmata firmware. The info is then interpreted using a Pure Data patch that maps the Arudino input to different points in the song. This allows the user to scrub forward and backward through the song.

All the technical specifications can be found here:

http://time.com/3902181/how-to-help-your-kids-say-goodbye/

YouTube / Rachel N – via Iframely

The app allows muscle car drivers to be more conscious of those around them by quieting their engine in a residential area for example. Though, it is interesting to consider whether, with this technology, neighborhoods will require muscle car drivers to quiet their famed and beloved engines. Then maybe the law requires that your engines be linked to a server that automatically quiets them in a residential area. Though this application seems common sense, at what point will regulation of what’s beloved and fun go to far When will the IoT give us too much control over the systems we use?

Van der Lee’s goal with Vinduino was to improve upon commercial soil monitoring systems that rely upon only one sensor by instead using three to better understand how water is moving through the soil.

Vinduino demonstrates how Aruduino and the IoT are allowing consumers to not only control and view their data but to demand better accuracy then commercially available. After talking with a grad student that has worked with wearable companies and hearing her commentary on how questionable some of the information gathered by commercial wearables can be, products like the Vinduino demonstrate that the most revolutionary wearable products in the future may be those that allow users to adjust to their own amount of accuracy.

Each of the portable environment monitors’ data is also streamed to a server where it can connect to the uRADMonitor network which “allows citizens around the world to collaborate on a vast, crowdsourced dataset of global air quality and radiation measurement”. This aspect of the project also allows users to demonstrate disparities throughout the country to emphasize the disparity in air quality in different parts of the world. If IoT devices continue to link our community to make change together, the possibilities are endless.