Patient movements can cause motion artifacts on physiological signals and can result in false alarms in a continuous patient monitoring environment. This thesis explores the use of centre of pressure (COP) signals from a pressure sensitive mat to (a) detect patient movement in real-time, and (b) classify the upper/lower directionality of movement. For (a), the sum-distance travelled by the COP is tracked over time using a sliding window with data from seven patients. Window-boundary-suppression led to improved motion detection with precision = 0.84 and recall = 0.71 with a window of 10 seconds. For (b), seven features were derived from the COP, and feature selection was done using out-of-bag-error ranking and sequential forward selection. It was found that using a sample imputation approach of adding ~13 minutes of hand-annotated new subject data to the training set makes the classifier most useful, producing accuracy = ~87.29%, precision = 0.90, and recall = 0.84.