Human activity recognition through deep learning: Leveraging unique and common feature fusion in wearable multi-sensor systems

With the progress in IoT and AI technologies, multi-sensor fusion for human activity recognition (HAR) has garnered considerable attention. As a result of integrating diverse information from different sensors, individ-uals employ sensors to monitor their daily activities. However, identifying crucial features for classification and assigning suitable weights to sensors is a complex task. On the other side, varied data structures pose challenges in establishing a unified format for the fusion of diverse data. To address these challenges, this paper presents UC Fusion, a method focusing on the fusion of unique and common features in wearable multi-sensor systems for HAR. First, UC Fusion merges the unique feature of each sensor with the common features of all sensors. Second, it tackles the challenge of handling heterogeneous data by unifying the data format through segmentation and dimensional transformation. Extensive experiments on the UCI HAR and WISDM datasets were conducted to evaluate UC Fusion's performance. The results demonstrate that our proposed method secured an average recognition accuracy of 96.84% and 98.85%. Furthermore, ablation studies were performed on each module of UC Fusion to assess their impact on accuracy, confirming the effectiveness of the proposed method.