Dynamic Multisensor Fusion Framework With Adaptive Spatiotemporal Optimization for IoT-Based Motion Recognition

Motion recognition in IoT-based sensor systems is crucial for applications such as healthcare and human-computer interaction. However, one key challenge—data structure inconsistencies—complicates the performance of existing systems, particularly in dynamic real-world environments. Traditional fusion approaches lack the adaptability required to address sensor inconsistencies and fail to fully leverage the potential of multisensor data. To overcome this challenge, we propose a dynamic multisensor fusion framework (DMSFF) with adaptive spatiotemporal optimization. This framework introduces a dynamic sensor weighting mechanism that prioritizes reliable data while suppressing noise, ensuring robustnesss. A transformer-based fusion architecture captures spatiotemporal features, modeling complex intersensor relationships and long-term dependencies. Additionally, a motion kernel matching module aligns the data with canonical motion patterns, improving feature extraction and enhancing the recognition of subtle activities. The framework is validated on benchmark datasets, including those with real-world noise and structural inconsistencies, achieving an accuracy of 99.48%. This work establishes a new benchmark for multisensor motion recognition, providing scalable and robust solutions for smart healthcare and human-computer interaction.