  Magnetically controlled capsule endoscope (MCCE) is an emerging tool for the
diagnosis of gastric diseases with the advantages of comfort, safety, and no
anesthesia. In this paper, we develop algorithms to detect and measure human
gastric peristalsis (contraction wave) using video sequences acquired by MCCE.
We develop a spatial-temporal deep learning algorithm to detect gastric
contraction waves and measure human gastric peristalsis periods. The quality of
MCCE video sequences is prone to camera motion. We design a camera motion
detector (CMD) to process the MCCE video sequences, mitigating the camera
movement during MCCE examination. To the best of our knowledge, we are the
first to propose computer vision-based solutions to detect and measure human
gastric peristalsis. Our methods have great potential in assisting the
diagnosis of gastric diseases by evaluating gastric motility.
