Step Detection Verification was our main focus for this week’s design review process.
This week was important to test our step detection methods as it is the base of our desires – to match a runner’s pace. Pace being steps/minute rather than speed of distance/time.
We used class time to research how accelerometer data is measured, differs, is calculated, and how we can verify them against each other. Additionally, this research included finding metrics for believed accuracy for the accelerometers we are considering to use. Additionally, we designed this test and verification process as follows with two users, Aarushi and Mayur (data sheet on our Google Drive):
I ran on a treadmill to (1) verify accelerometer data, and (2) to measure my tolerance for gap between starting run and the music adjusting tempo to pace. For jogs of 20-40 minutes (3-5 miles) at more or less the same pace, my tolerance for not adjusted music was 3 minutes. For runs of 10 minutes (1-1.5 miles) at more or less the same pace, my tolerance for not adjusted music was 1.5 minutes.
When verifying accelerometer data, we compare between two android phones of different generations and a smartwatch. This design was controlled by manually counting steps while running, and using all devices on the same run. These measurements were done for 30 second, and 1 minute intervals at speeds of 5.5mph to 10mph at intervals of 0.5. Additionally, I completed three ‘long’ distance runs of 3 minutes and 5 minutes for step verification, and longer for tolerance of gap between starting run and the music adjusting tempo to pace. (A tragic event because I prefer intervals to distance). An iphone was attempted for comparable metrics, but the iphone 7 plus was what we had access to, and only updates every 10 minutes. Thus, it was impossible to use to measure the number of steps in a defined time interval.
We figured this would be a technology that could jeopardize our project if the data we got from the phones and watch weren’t good enough. Out contingency plan was to either use a Bluetooth pedometer or write our own step detection algorithm, however we found that the data we got from the newer Android phone lied within an average 4% error of the actual step count which makes us confident in using it.
The biggest change that we are considering is not using the watch since the error rate on average was roughly 10-15% which is a little higher than we liked. We are thinking of making the app for the watch, but still using all the data from the phone.
Software Decisions with Wavelet Transforms
During class time, we researched best methods for phone & watch applications – Java. Python would be used for wavelet transforms for our familiarity and ease of use. Integration via Jython is possible.
