Rhythm Listener

Uncategorized — rbrill @ 12:27 am

Password: mti


This project is one part research tool, one part affective computing device. It has two goals. The first is to explore the relationship between computer and physiological excitement. The second is to be able to manipulate excitement at will.

All too often, I find it hard to transition to other activities after using my computer. This is especially true late at night, when my will to resist the Internet’s temptations is at its lowest. Our use of the internet often leads us into compulsive loops, in which the appeal of small hits of dopamine cause us to act quickly and without thought.

I wanted to build a tool that would help users quantify the impact that different computer-based activities have on their body and affect. My hope is that by gathering data on this activity computer users can form healthier habits.

Heart rate is a good and commonly used proxy for physiological excitement. Studies also show a correlation between steadier of heart rates and the ability to regulate of behavior. For these reasons, I chose to use heart rate data to power my project and christened it “Rhythm Listener.”

Inspiration/Related Work

There exists a small body of scholarly work on the effect of music on heart rate, examples of which I have listed below. A number of student projects have translated heart rate into musical output, but none that I found have used music to affect heart rate in such a controlled manner.


  1. Heart Rate Variability and likelihood to engage in Addictive Behavior:
  2. kb.osu.edu/dspace/bitstream/handle/1811/60413/LindsayCannon_HonorsThesis.pdf?sequence=1
  3. Pitch > Rhythm in influencing emotions: www.jstor.org/stable/40285907
  4. Effects of music on physiological arousal: www.theaudioprof.com/pubs/2007_1.pdf
  5. Cardiovascular responses to music tempo during steady-state exercise: www.asep.org/asep/asep/Birnbaum%2012(1)50-56.doc
  6. Music, heart rate and the medical field: www.emerginginvestigators.org/2013/04/the-effect-of-music-on-heart-rate/



Rhythm Listener consists of an Easy Pulse v1.01 heart rate sensor attached to a computer mouse on one end and to an Arduino microcontroller on the other. Heart rate data is sent to one of Arduino’s Analog In pins. Voltage output to the Arduino varies according to the relative thickness of the blood in the finger being read.









Arduino has a serial connection to the user’s computer using a USB cable. I enclosed the Arduino and heart rate signal processing chip inside a plastic case. The wire to the reader and the USB to the computer both come out of this case, resulting in a look like that of a standard computer mouse (albeit one with a large adapter).


The signal from the Arduino is routed to a Max Patch using standard Firmata protocol. I use “Maxuino” to accept the serial output from the Arduino and process it in Max. The result is sent to a slider, which outputs a value between 0.00 and 1.00 according to the heart rate. I take this data and route it to a graph of the raw heart rate data and to a sub-patch I built that calculates beats per minute from the time between peak values. I display both the raw data and the BPM over time in graphs for the user to easily visualize.

When the user presses a Max button, a conditional is triggered that reads their current heart rates and plays a song segment with a similar BPM. I can then adjust the speed of the song up or down according to preset preferences.

Lessons Learned

As happened throughout this course, I taught myself a new programming language in the process of doing this project. One of my biggest struggles was getting a basic understanding of Max and the specific objects I needed to use for this project. It is now hard to believe that I ever struggled with this, but the initial learning curve was steep. Once I had the lay of the land I found Max to be a joy to work in.

Getting the serial connection between the Arduino and Max working was and still is the biggest challenge of this project. I tried three different methods (Max2Arduino, Serial OSC and finally Maxuino) before getting Maxuino to work.

Unfortunately, this serial connection stopped working for unknown reasons right before our show at Assemble. This was especially troubling because I had everything running 40 minutes earlier, and changed nothing but my location in that time. I have not been able to fix it, nor has anyone else who I have asked for help from.

Fortunately, I had a backup project that I was able to show at the live demonstration.

The video above is how the program would have worked had the serial connection not failed.

Compulsion Loops and the Digital Age: www.theatlantic.com/health/archive/2012/07/exploiting-the-neuroscience-of-internet-addiction/259820/?single_page=true
EasyPulse sensor website: www.emerginginvestigators.org/2013/04/the-effect-of-music-on-heart-rate/

Precedent Analysis

Uncategorized — rbrill @ 11:16 pm




I’m should receive a Myo developer kit from Thalmic Labs this week. I’m interested in exploring the capabilities of this input device. Gestural controls have interested me for some time. Beyond their ability to allow more natural motion to control a computer, they bring

I am interested in the social and psychological implications of the added physicality they enable in our interaction with computers. In particular, Keeping Together in Time: Dance and Drill in Human History by William McNeil and Interaction Ritual Chains by Randall Collins come to mind. Both works discuss the role of rhythmic movement in creating solidarity in small groups.



1) Keeping Together in Time: Dance and Drill in Human History by William McNeil


2) Randall Collins: Interaction Ritual Chains (2004)

Book Review: www.cjsonline.ca/reviews/interactionritual.html


3) World Kit by Chris





Project 2.01 – Oatcam

Uncategorized — rbrill @ 11:24 am

Making Things Interactive: Assignment 2 from Ryan B. on Vimeo.

For our second project, we were tasked with making a spy device using Raspberry Pi. This was my and Danita’s first time working with Raspberry Pi.

We created a motion sensing spy device using the Raspberry Pi, an oatmeal container, a PS3 Eye camera and a passive infrared (PIR) sensor. We used the WiringPi mapping for the GPIO pins to take in data from the PIR sensor and camera. It took us a while to get consistent results with these pins until we realized that one of the pins we were using had a built in pull up resistor. Changing pins did the trick.

We definitely pushed the Raspberry Pi’s processing power to its limits at times in the development process, but in the end we had a system that can consistently capture and store and transfer photos of passers by.

by Ryan and Danita

Flicker Deck

Uncategorized — rbrill @ 11:37 am

Flicker Deck Demonstration from Ryan B. on Vimeo.



Skateboarding is fun, but participating in the sport comes with a good deal of risk. The biggest dangers are uneven pavement and passing cars. Uneven and cracked pavement reduces the control of skateboarders and can cause crashes in cases of extreme speed and very poor asphalt. Cars often do not see skateboarders or do not know how much space to give them.

Flicker Deck highlights rough pavement as a skateboarder passes over it. LEDs provide a visual indicator of rough pavement to those riding behind the owner of a Flicker Deck and surrounding cars. This information lets other riders better plan their path downhill and lets nearby cars know that the skateboarder has less control and should be given more attention or a wider berth.

Flicker Deck users a small microphone to translate the vibrations created by riding over rough pavement to red light. I chose red light for this project because it mimics the behavior of car brake lights and can take advantage of an already existing mental model. Drivers are trained to slow down when they see red brake lights suddenly brighten in front of them

Flicker Deck was inspired by Project Aura, a bike light that illuminates wheels based on the speed of the cyclist.

I was also interested in the idea of translating the micro-topography of our roads into another form of information. Long boarders gain a very intimate knowledge of the characteristics of the roads around them, and I like the idea of sharing that knowledge with others.

If I were going to extend this project, I would add a module that tracks and stores location and vibration data and use the information gathered over time to map pavement quality in an area. This information could be used by other long boarders to plan routes and by local governments to plan road maintenance.

How it Works

A protective case secured to the bottom of the deck houses a microprocessor, a 9V battery and microphone. The microphone is secured behind a small hole cut in the side of the case. This hold provides clearer input for the microphone and lets it pick up the noise made by passing cars.

The microprocessor takes the amplitude of the sound picked up by the microphone and outputs a corresponding level of power to a string of LEDs that emerge from a small slot cut into the case and wrap around the bottom of the skateboard. By wrapping the LEDs around the trucks of the board I was able to get the translucent wheels to glow along with the ground.

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