Applied persistent homology, a toolset to analyse high-dimensional data using topological data analysis, to motor control, which includes stride-to-stride fluctuations and gait dynamics, and neurodegenerative diseases.
In this project, we built an ultra-low power backscatter device for monitoring Atlanta’s urban heat islands, using digital communications techniques to extend the range between the tag and transmitter/receiver by sacrificing throughput. We explore digital range-increasing techniques such as forward error correction (FEC) and investigate low-power digitization and signal generation. We build a system consisting of a co-located transmitter and receiver capable of reading temperature data from a single backscatter tag.
mmWave radar data is rich in information related to humans like pose and movement. We also observe that collection of data is easy, however labelling the data is extremely hard. In this project, we have built an unsupervised framework to extract spatial and temporal features of the data which are critical to downstream supervised tasks like pose estimation, tracking and identifying individuals. Using the latent space features, we are able to build more effective supervised learning frameworks that require 50 times fewer training samples than prior work.