Deep similarity segmentation model for sensor-based activity recognition

Signal segmentation is a critical stage in activity recognition. Most existing studies adopted the fixed-size sliding window method for this stage. However, the fixed-size sliding window may not produce the most effective segmentation method since human activities have variable length durations, particularly transitional activities. In this paper, we propose a novel deep similarity segmentation model that overcomes not only the limitations of the fixed sliding window method but also the weaknesses of threshold-based segmentation methods. Specifically, a novel deep learning model is designed to distinguish between transitional and basic activity by treating the segmentation task as a binary classification task. The proposed model accepts multiple sequence windows and extracts the local features automatically for each window using convolutional neural networks. The temporal features of windows are extracted by measuring the similarity and differentiation between the local features of adjacent windows. The local features are combined with the temporal features and passed to deep fully connected layers to distinguish the transitional activity from the basic activity windows. The evaluation relies on two public datasets, SBHARPT and FORTH-TRACE. According to the experimental findings, the proposed approach can distinguish between basic and transitional activities with an accuracy of 98.51% and 98.41%, respectively. Additionally, our method outperformed the fixed sliding window for activity recognition by 2.93% and 2.24% for both datasets, respectively, achieving an accuracy of 93.35% and 84.96%. These results are significant and outperform the precision of cutting-edge models.