When calculating features in activity recognition, applying a fixed-length sliding window to time series data without considering the timing of activity transitions (hereinafter referred to as activity change points) may result in multiple activities being mixed within a single window, thereby reducing recognition accuracy. To address this issue, we propose a segmentation method that utilizes plantar pressure sensors placed on the foot to automatically detect moments of foot-ground contact. Since these contact moments often correspond to the boundaries between distinct lower-body movements, they can be used to segment continuous activity data into more homogeneous segments. This approach helps reduce the likelihood of mixed activities within a single analysis window. In our evaluation, we compared the proposed method with a conventional segmentation approach based on the spectral transition measure of acceleration data. Although the proposed method did not outperform the conventional method in terms of overall segmentation or activity recognition accuracy, it showed better segmentation performance in specific transitions characterized by distinct foot-ground contact patterns, such as transitions from sitting to walking or from walking to ascending stairs. These findings suggest that plantar pressure–based segmentation can serve as a valuable supplement to existing approaches, particularly in scenarios involving lower-limb activity transitions.