over the past 3 weeks i’ve been collecting data on my movements with some simple android apps i’ve developed. 1) measures and records my location (in latitude/longitude) every 10 minutes or every 10 yards i move, 2) measures my movement based on the internal accelerometer in the android.
data logging the accelerometer has been a bit difficult as the deluge of sensor information produces a huge string of data text that crashes the application when it tries to POST the data to a simple sinatra web app. but i was able to grab a handful of data points to begin my initial analysis ::
what does movement look like?
using the R statistical programming language i was able to find a pattern to my walking movement along the xyz. compared to sitting [which is obviously a flatter line], based on the frequency of the peaks we can determine the rate of bounce in my movement. thus faster movements like running have steeper peaks with short intervals between them vs more languid movements like biking which i hypothesize will be smoother with wider ranges between the peaks.
what this means in terms of my application is that the android app could determine the type of activity the user is doing based on the xyz data — this could then become a pedometer [to measure the number of steps], count the number of calories burned [while passively monitoring the user], or even with machine learning techniques learn the specific patterns of movement and offer suggestions [i walk to the subway every morning to school -- based on the timestamp + lat/long the app knows i am on my way to the subway, and passed on my movement the urgency of getting a train -- so perhaps the app retrieves the train schedule for my rushed commute, etc].
also, in terms of aesthetic i was really inspired by yesyesno’s project to create paintings based on nike+ geo-location data of a runner’s movement.
as well as steffen fiedler’s instruments of politeness – a kinetic sculpture that expressed movement from the perspective of a phone.
the cam wheels that operated the phone’s movements were themselves beautiful abstract visualizations of movement.
CAM WHEELS: inactive, walking, running, cycling, nervous, drunk at the bus stop (from left to right)
i’m still working with the location data right now — plotting to a map of the east coast my movements for the past 3 weeks. still figuring out the tricks of R but there should be a brief video of my 3 weeks of movement up soon.
