Transition-Aware Detection of Modes of Locomotion and Transportation Through Hierarchical Segmentation

Recognizing human daily activities with motion sensors data, specifically, modes of locomotion and transportation provides important contextual information that enhances the effectiveness of mobile applications. For instance, in assisted living or sports monitoring it is essential to log driving or running episodes. Previous studies in this field have utilized a fixed-size windowing technique for segmenting the sequential data of sensors. While segmenting signals into larger windows provides more information about the signal for classifiers, it increases misclassification rate when a transition occurs between the activities (i.e., multi-class windows). This will lead to inaccurate detection and logging of the activities of interest. To identify the exact time of transition from one to another activity as precisely as possible, this article proposes a fast and efficient hierarchical search algorithm that finds the exact sample at which transition occurs. This search algorithm can be applied to any activity recognition model with various lengths of segmentation window. To further improve the performance, we propose a new structure of 2D signal inputs to be used with 2D convolutional neural networks (CNN), which improves the ability of the CNN in capturing patterns underlying in inter-axes correlations. Experimental results show that the proposed transition detection method improves the F1-score by 28% compared to using fixed-size windowing approach for multi-class windows. In addition, the proposed method is 20 times faster than the naïve search.