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For my final project, I'm going to try and build a personal archival system that will help me to capture a more full picture of my online life.

Archiving My Online Life

I’m interested in the data that we share publicly across our social networks. Each day we reveal special and ordinary moments in our lives with different groups of “friends.” Sometimes it’s a brief status update on Facebook, others it’s a link on Twitter, a picture on Flickr, an article from The New York Times or a place we’ve visited on Foursquare.

But these moments are ephemeral and – once shared – often forgotten.

And when these moments pass, they are difficult to re-visit. Most social networks don’t give users access to their full histories. Others shut down (Jaiku, anyone?). Still others fall out of favor and once discarded, are difficult to revive.

The sites that do allow you to look back are often strictly organized so that you can only view your history in a linear fashion or by choosing a keyword. And for these systems, it can be hard to convey an emotion.

For my final project, I’m going to try and build a personal archival system that will help me to capture a more full picture of my online life. Once I have all this data, my goal is to create an interesting visualization that will allow me to re-visit moments from my past in a non-linear fashion and provide some context to trigger feelings of nostalgia.

Although this prototype will likely only pull data from the last year, this project will be much more interesting in several years when I have a huge database to draw from.

You can view the slides from my presentation here: Final Project Proposal
(NOTE: Give it a second to load or clicking the forward button may not work)

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For my research presentation, I chose to look at how nostalgia is captured, interpreted and portrayed in digital art.

Exploring Nostalgia Online

For my research presentation, I chose to look at how nostalgia is captured, interpreted and portrayed in digital art. Nostalgia is a difficult emotion to work with because it is most often conjured by sound (i.e. music) or smell (i.e. grandma’s house). However, it also stems from certain events, such as looking at yearbooks or attending a reunion of old friends. So how do you take nostalgic moments from artificial environments and create a piece of art that evokes this emotion? Can we create nostalgic moments from looking at our online selves, or is this a feeling firmly rooted in real world events?

There are a bunch of artists that are using digital media, social networks and other online data to create evocative visualizations as well as multimedia pieces and physical installations. Some of my favorites and links to their work are included in this presentation:

ROY Research on Nostalgia

(NOTE: Give the movie time to load before you start clicking through or it might freeze)

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"Capturing Attention," is a piece of networked art that observes the observer and records the first moment at which they contemplate the artwork.

Capturing Attention

It’s fascinating what we choose to pay attention to, and what really constitutes attention. This project, titled “Capturing Attention,” is a piece of networked art that observes the observer and records the first moment at which they contemplate the artwork.

In my first attempt, I tried to embed a camera inside a picture frame. When the visitor directs his attention at the picture for more than a five seconds (to indicate that it is actually attention and not just a passing glance), the camera takes a picture of the viewer and then uploads it to a website where people can view a gallery of people in that first moment of observation.

Through this process, perhaps we can gain a better understanding of how we consider the world around us. For those visiting the website, perhaps they too can gain insight into how people react to art, what faces they make when the view a piece, and how long a piece can sustain their attention.

I started off by creating a Processing sketch that could conduct the face detection. The OpenCV library has an excellent sketch example to build from. There was a bunch of tweaking to the code so that it only detected faces after a given period of time elapsed, and only from a certain distance. The camera is a hacked PSEye.

I then implemented the PDF2Web library to save the captured images to a file on the server. Finally, I wrote a PHP script to pull the images from the server and display them on a page that uses Javascript to dynamically load the images and keep checking to see if any new images are added to display.

Unfortunately, I couldn’t find a satisfactory method for disguising the camera. Any one instance of a lens in a frame is obvious, and multiple dummy lenses embedded to distract from the lens capturing the image still raises awareness about the presence of a camera which changes the interaction. There’s also the issue of hiding the cord. This may be easier depending on the environment, but in this particular case, it was impossible.

But the first camera in the first frame is only the first part of the project. What really becomes interesting to me is when you have multiple frames, all similar, housing different types of art. It would be cool to be able to capture reactions to each piece and create a new work that aggregates people’s reactions.

My next thought was that I could hide the camera inside the artwork itself. I mocked up a quick version at home with the materials at hand:

This is just a box containing the camera and a few LEDs. But once I covered it all up with an old 3M privacy screen, the camera was completely hidden. For the next iteration, I drew inspiration from Eyal’s piece outside the JRoom. With some foamcore and black masking tape, I created the latest version:

It’s actually only the black box, and not the stuff that it’s sitting on. Once I had a piece of art that I thought would attract attention, I started playing with the images. At first, the images were the same as those you see with face detection: gray with a red rectangle around the face:

They work for face detection, but they’re not very pretty. However, this method did help me to see that I needed to play with the settings to avoid false-positives:

After I started tinkering with the housing for the camera, I stumbled upon a Hipstomatic type look that I really like, entirely by accident:

The last part that I should touch on is the web interface. This is an important element of the project for me because it takes this moment of attention that might otherwise be forgotten, captures it, and re-contextualizes that time for a totally different audience. On one hand, I think that everyone that looks at a piece of art does so with the same expression, one that is both inquisitive and perplexed. Then a moment of recognition or understanding settles in and an opinion begins to form. This moment that I’m capturing occurs just before that.

Visitors to the website will see a pixelated box on the left hand side of the screen with squares of varying colors. Each square represents an hour in the day (there are 24 boxes). The color is determined with some degree of randomness based on the number of pictures captured in that hour. A white box indicates no images. Clicking on the box will reveal all of the images from that hour on the right side of the page, and scrolling over the images enlarges them.

If I were to work on the site further, I would probably add more ways to view the pixels on the left (by minute, by second) and perhaps some more information about each image on the right.

I’ve learned many lessons from this first go round. The camera height is important for face detection as well as for making sure people “notice” the work. Someone gave me the idea of putting a card with some small text in front of the display to entice people to get close, which is not a bad idea. I also need to consider whether I want people to know their picture is being captured and displayed on the Web, or whether that’s a totally different part of the experience.

Posted in Rest of You, Understanding Networks

For this week's assignment, we explored the information we create and edit with our keyboard through some basic keystroke logging and visualizations.

Keylogging

For this week’s assignment, we explored the information we create and edit with our keyboard through some basic keystroke logging and visualizations.

As anticipated, I learned a lot about what words I use the most often, which you can see on the left. The Processing sketch had some difficulty parsing a bunch of my text, which is why this list is so short. I tracked all my keystrokes for five days, and in that time I did a lot of coding. Most of the coding language doesn’t appear here because it wasn’t read by the program. I seem to use the word “I” a ton, so now I’m conscious of it. Thanks. There’s also a lot of “to” being thrown around, so I’m going to be more conscious of that as well.

After generating a list of words, I filtered them by selecting only those that appeared 10 or more times, which I then placed into a Processing visualization that you can see on the left. The actual sketch uses the Peasy cam library to create a 3D node of the words I used which can then be viewed from any angle. It’s a modified version of this sketch by Tiemen Rapati.

What’s cool about this to me is that I’m able to interact with my conscious self (i.e. the person who created the text) in a totally different way. The node illustration feels like a new way of searching through my brain and an opportunity to revisit a previous version of my thoughts. In the next iteration, I’d like to change the size of the node based on the number of times the word appears.

But more interesting to me is how LITTLE I type. I tend to use a lot of keyboard shortcuts and I tend to click around around opening programs and surfing websites, as illustrated in this generated sketch:

This sketch represents the movement of my mouse over a 7.5 hour period. I actually did this seven times in 7.5 hour intervals to compare how much time I spend in front of the computer and what I do during that time. The larger black circles represent periods of time when my mouse was at rest, which means I was either away from the computer or I was (more likely) watching a TV show or YouTube video. I’m not really sure what I learned from this, except that there was a lot more activity with my mouse than with my keyboard. However, the patterns that were created in each of these sketches, while very cool to look at, don’t really provide me with as much useful data as the key logging.

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The second part of my tutorial on how to build a ghost detector.

Building A Ghost Detector Part II

I was able to get the Logomatic to start writing to the file, which was very exciting. It now creates a text file with values ranging from 0-1023. Next, I wanted to attach two sensors: a GSR and a temperature sensor.

The temperature sensor was pretty easy. After testing it on the breadboard, I soldered it on to the perf board and then tested it on one of the pins. It worked the first time and was able to record data. However, the GSR gave me a lot of trouble.

GSR Sensor

I decided to skip pennies and go straight for the good stuff: electrodes.

This is what they look like:

The problem is that you can’t just connect them to the breadboard or the logomatic. You have to first use hot glue or electrical tape to keep the wires from coming out of the plugs. I spent a few hours experimenting with many different methods, and I couldn’t get any to stay in place. When I tried sewing the electrodes into a sweatshirt so that I could wear them surreptitiously, the slightest pull yanked them from their connection points. I went back and bought this connector:

In theory, the connection would now be more secure because the red and black male headers fit directly into the electrode female headers. But of course, nothing is so simple. I then had to cut off the opposite end and solder the wires to create my circuit. Luckily Mustafa had a set he had soldered for me to use as an example:

I settled for attaching one sensor and going mobile for proof of concept. The device is pretty small and it collects a ton of data. After recording for only an hour, I had a text file that was pretty large to parse. Here’s what the final mobile unit looks like:

No ghosts were found this week!

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Building a Ghost Detector Part I

With Halloween only a few weeks away, I started thinking about ghosts and how people can “sense” their presence even though they are invisible. So I decided that for my mobile data logging project this week, I would try and predict the presence of paranormal activity by evaluating the environmental conditions in my locale. I decided to build a ghost detector.

I started off by doing some research into how people detect the presence of a ghost. There are a few factors that are consistent across a broad spectrum of ghost hunters and people that claim to have witnessed a ghost in their proximity: a rapid change in room temperature, increases in heart rate and breathing, strange sounds and visuals. All these things can be sensed with the tools we have available to us. In addition, I looked into getting a Geiger counter which is used by many ghost hunters to detect abnormal electromagnetic fields (EMF).

The first part of the week I spent trying to get the Logomatic V2 datalogger I purchased from SparkFun to read/write (i.e. log data) as described on the product page. Despite close to 10 hours of experimenting with different settings on the config file, a few trips to Radio Shack, extensive forum research and two hours with the resident researchers, the thing just won’t work. It’s possible that I need a 1GB microSD card instead of a 2GB, but those are hard to find. Otherwise, I’m not sure what else it could be at this point. I’ll spend some more time on it next week, but the time suck forced me to reexamine my objectives.

Next I decided to use my Arduino and carry around two sensors with my netbook in my backpack as a proof of concept. I used a LM34 to sense environmental temperature and two electrodes sewed into my sweatshirt to measure GSR:

I tested the code in both Arduino and Processing, but when I returned after several hours of data logging, I discovered that I was getting an error in the Processing sketch and that the GSR sensors weren’t feeding me data. This could have happened when I was sewing the electrodes into the hoodie or it could have been as a result of shuffling around in the backpack.

While the outcome wasn’t exactly as I had hoped, it did encourage me to move forward on this project. I think that if I can get the Logomatic working or find an alternative, lightweight and easy to use datalogger in its place, I should be able to construct a working prototype for next week.

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This week was about learning how to visualize the data I collected previously using two sensors.

What Sitting Looks Like

This week was about learning how to visualize the data I collected previously using two sensors. Armed with the data set from the previous week’s lab, I figured out how to parse both a .txt and a .csv file using the code supplied by Dan and some other code snippets from Visualizing Data.

When I tried last week’s assignment, I had started using an FSR by itself for about an hour before I got the GSR sensor working, and because the code appended the data from when I was using one sensor to the two sensor data set in the text file, I had some funky errors since there were added fields. Once I got that sorted, it was pretty straightforward to parse the data.

An interactive graph makes it easy to go through and view the data and compare two sensor sources, but to make this useful, I really need a third line that shows what I was doing during these different time periods. Otherwise, it doesn’t really reveal too much about the results. Also, while this is useful for general understanding of the sensors and visualizing the data set, it’s not terribly interesting for most people to scroll through. Just building this gave me a better sense of how I want to approach my next project. It really is important to start with a question and prepare how you want the data to be interacted with BEFORE you start.

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For this week's lab, I wanted to measure how much I fidget while sitting in one place for a long time as well as how I react to information I read and in my surroundings.

Examining Focus and Excitement

roy_week2

For this week’s lab, I wanted to measure how much I fidget while sitting in one place for a long time as well as how I react to information I read and in my surroundings. I started by taping a force sensitive resistor (FSR) to my chair. My thinking was that I could see how much I squirm in my seat. I spent the first hour sitting and working on a few projects on my computer, totally immersed. At that point, I took a break to stretch and walk around.

When I returned, I had a cup of coffee (which led to another break to pee 45 minutes later). The second hour I spent a lot more time watching how shifting in my seat created a gradual curve in the sensor reading as the pressure on the resistor was eased. What I didn’t take into consideration was that by taping the resistor to my chair, I didn’t necessarily sit on it the same way in session one and session two.

With the GSR, I purchased two electrodes and put them on either side of the pulse on my wrist. For a better connection, I used a sweatband to keep it in place. There wasn’t a lot of movement on the screen readout during either hour, except when I moved my wrist close to the computer. It didn’t change based on what I was reading, who I talked to on the phone, or who came up to talk to me where I was sitting. More research indicated that I needed an op-amp to get a stronger reading next time.

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What was interesting to me about this week's readings and videos was Dan Ariely's TED talk on the ability to manipulate one's unconscious mind through cognitive illusions.

Cognitive and Temporal Illusions

What was interesting to me about this week’s readings and videos was Dan Ariely’s TED talk on the ability to manipulate one’s unconscious mind through cognitive illusions. At one point he talks about how we don’t know our own preferences, and we are susceptible to “options.” This made me think about Tom’s Shoes.

When you purchase a pair of their shoes, Tom’s will donate a second pair to a child in need somewhere in the developing world. Now the idea that by simply doing something that you were going to do anyway (purchase a pair of shoes) allows you to have a positive affect on someone you don’ even know is attractive. At first blush, I really liked the concept. It feels altruistic, even if you will never know the person benefiting from your purchase. Or even if there WILL be a donation. In actuality, the purchase might help a child by giving him a pair of shoes, but it might also hurt local cobblers who will no longer be able to sell their shoes since people are receiving them for free.

What if instead of advertising that buying a pair of shoes would benefit a child in a developing country it stated that buying a pair of Tom’s Shoes would take a job from an adult in a developing country? Both are true. Would you still buy their product? Perception is interesting in this way.

Albert Einstein said of relativity, “Put your hand on a hot stove for a minute, and it seems like an hour. Sit with a pretty girl for an hour, and it seems like a minute.” Time is another strange illusion. How we spend it determines how we feel about it. Time is a commodity. Your time is “valuable,” and something that you “spend.” Even if you don’t think about it, you will reflect on what you did and conclude whether it was “time well spent.” Our unconscious connection to Time as resource, as commodity and our emotional response to it (stressed, excited, sad) is another area I’d like to explore further.

Posted in Rest of You