<\/p>\n
Step Detection<\/strong><\/p>\n 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n <\/p>\n Wavelet Transform<\/strong><\/p>\n Working on the wavelet transform model\u00a0 based off the paper for musical analysis and audio compression methods: https:\/\/www.hindawi.com\/journals\/jece\/2008\/346767\/#experimental-procedures-and-results. This was decided after evaluating numerous methods that were also discussed in this paper. This paper provides research and insights on testing how a transformation can be deemed successful. They proved that it is effective in decreasing error, as seen as quantization artifacts or Signal-to-mask ratio (SMR). This music transformation was performed by Discrete Wavelet Packet Transform (DWPT) for its increased accuracy and less computational complexity. I will follow suite for these two beneficial distinctions.<\/p>\n I will be implementing this in Python for easy integration into Java via Jython. Therefore, I have been playing around with Python’s wavelet transform open-source library – pywaveletes. I have setup my environment deleting\/installing all necessary libraries and their correct versions for this testing. I have started testing the wavelet transform functions of this library on basic signals like [1,2,3,4] ,\u00a0originalSignal = sin(2 * np.pi * 7 * originalTime) where originalTime is a linespace of time from -1 to 1 broken up into ‘discrete’ components of 0.01 increments in time, and <\/span>images, since I have worked with wavelet transforms with images before. This experimentation will continue into Saturday night, however this update will be submitted before results with audio signals are tested.<\/p>\n <\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Step Detection 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. I ran on a treadmill to (1) verify accelerometer data, and (2) to measure my tolerance for gap between starting … <\/p>\n