A Learning-Based Multi-Node Fusion Positioning Method Using Wearable Inertial Sensors

This study presents a novel approach to enhance the accuracy and adaptability of pedestrian positioning by fusing data from multiple Inertial Measurement Units (IMUs) attached to the human body. Leveraging the temporal and spatial richness of IMU data, our proposed multi-node sensors fusion strategy accommodates multiple sensor placements and various motion modes, a challenge that has posed difficulties for previous methods. The methodology is based on data-driven principles, directly estimating position information from IMU data. The core of our method is the strategic allocation of weights to optimize the contribution of each sensor based on reliability and relevance. Two restraint strategies, Gumbel Softmax Resampling and Empirical Risk Minimization under Fairness Constraints, are introduced to enhance fusion accuracy and robustness. Experimental results show improvements in positioning accuracy and robust performance in various situations compared to existing methods. Our approach is expected to be applied in domains such as indoor navigation and motion analysis, highlighting the key role of wearable sensors in advancing positioning accuracy and addressing complex scenarios.