This past week I was responsible for presenting our project during the Design Review. As a result, I spent most of the time during the first half of the week refining the presentation as well as practicing my delivery. After that, since we are ahead of schedule in terms of the CV system implementation, I focused on doing research into the specific algorithms and optimization methods we can use to construct our 3D comparison engine. Since we want to provide feedback in a timely manner, whether that’s after the entire dance or real-time, computation speed is a big problem for us since our chosen algorithm (DTW) is extremely computationally intensive. Therefore, I spent time looking specifically into optimization methods that include papers written on PrunedDTW, FastDTW, SparseDTW, etc.
Illustration of DTW:
Implementation of standard DTW:
Example of SparseDTW:
Al-Naymat, G., Chawla, S., Taheri, J. (2012). SparseDTW: A Novel Approach to Speed up Dynamic Time Warping.
Olsen, NL; Markussen, B; Raket, LL (2018), “Simultaneous inference for misaligned multivariate functional data”, Journal of the Royal Statistical Society, Series C, 67 (5): 1147–76, arXiv:1606.03295, doi:10.1111/rssc.12276, S2CID 88515233