Humans are colonized by trillions of microbes that compose the human microbiome. Much of the microbiome inhabits the mucus layers. Mucus layers, covering digestive, reproductive, ocular, and respiratory tracts, are viscous networks consisting mainly of water and mucin glycoproteins. Mucins assemble into a dense, cross-linked network that can affect bacterial swimming patterns, and studying this behavior provides valuable insights into how the body regulates interactions with both harmful and beneficial microbes. Here we present the use of time-lapse imaging to track individual bacterial cells within mucin and discuss techniques for accurately extracting cell trajectory data from these images. By integrating theoretical and experimental approaches, we also describe how to quantify bacterial movement in terms of speed, persistence, and randomness.