MAPIN: Mobility Adapted Pedestrian Inertial Navigation Using Smartphones for Enhanced Travel of the Visually Impaired

By using only accelerometers, gyroscopes, and magnetometers, inertial navigation systems can continuously track a pedestrian’s position without relying on external signals, making them a smart choice for seamless location-based services. While many AI-based pedestrian inertial navigation models already exist, most of them are generic and trained on a large amount of data. These models face accuracy limitations due to their inability to account for individual walking characteristics and various scenarios. In this paper, we propose a novel approach to pedestrian inertial navigation tailored to individual users. Our method uses gait segmentation techniques to leverage the cyclical nature of human locomotion, allowing for personalized modeling of walking patterns. A key advantage of our approach is its ability to achieve high accuracy with relatively small training sets—about 36 times smaller than the dataset used to train the state-of-the-art model, RoNIN. Through extensive real-world evaluation, covering 42.6 km with seven profiles learned from six volunteers, we show that our tailored models consistently outperform the RoNIN algorithm. Specifically, we report a stride length error of $0.07 \pm 0.07 \mathrm{~m}$ and a stride angular error of 7.72° ± 7.07°. Furthermore, the proposed method, when evaluated against a real-time implementation of another state-of-the-art generic model, IMUNet, shows superior performance in both walking distance and direction estimations.