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The objective of this work is to determine various modes of locomotion and in particular identify the transition time from one mode of locomotion to another as accurately as possible. Recognizing human daily activities, specifically modes of locomotion and transportation, with smartphones provides important contextual insight that can enhance the effectiveness of many mobile applications. In particular, determining any transition from one mode of operation to another empowers applications to react in a timely manner to this contextual insight. Previous studies on activity recognition have utilized various fixed window sizes for signal segmentation and feature extraction. While extracting features from larger window size provides richer information to classifiers, it increases misclassification rate when a transition occurs in the middle of windows as the classifier assigns only one label to all samples within a window. This …