Similarity Segmentation Approach for Sensor-Based Activity Recognition

The fixed sliding window is the commonly used technique for signal segmentation in human activity recognition (HAR). However, the fixed sliding window may not produce optimal segmentation because human activities have varying durations, especially for transitional activities (TAs). This is because a large window size may contain activity signals belonging to different activities, and a small window size may split the activity signal into multiple windows. Furthermore, the fixed sliding window does not consider the relationship between adjacent windows, which may affect the performance of the HAR model. In this study, we propose a similarity segmentation approach (SSA) that exploits the temporal structure of the activity signal within the window segmentation process. Specifically, the proposed approach segments each window into subwindows and extracts the inner features by measuring the similarity between them. The inner features are used to measure the dissimilarity between the adjacent windows. The proposed approach is able to distinguish between transitional and nontransitional windows, which achieves more effective segmentation and classification processes. Two public datasets are used for the evaluation. The experimental results show that the proposed approach can distinguish TAs from basic activities (BAs) at 97.65% accuracy, which enhanced the accuracy of TAs recognition compared to the fixed sliding window by 33.41%. Also, our approach achieved accuracy for activity recognition of 92.71% and 86.65% for both datasets, respectively, which exceeds the fixed sliding window by 2.29% and 3.93% for both datasets, respectively. These results are significant and exceed the accuracy of the state-of-the-art models.