An Improved Method for Multi-Motion Mode Recognition and Navigation Optimization for Indoor Pedestrians

The wearable indoor pedestrian navigation method based on foot binding inertial measurement units and zero velocity update (ZUPT) method as the core principle has a good application prospect. At present, indoor pedestrian navigation methods mainly study the navigation of pedestrians during walking movements. However, when performing special tasks such as rescue and medical search, there are usually multiple motion modes such as running, going upstairs, and going downstairs, which can affect the dynamic performance of wearable indoor pedestrian navigation methods. For indoor pedestrian navigation with some similarities, based on a multi-node wearable inertial sensor network, this paper proposes a multi-level hierarchical motion modes recognition method based on an optimized long short-term memory network using a sparrow search algorithm; In response to the problems of error divergence during long-term navigation in wearable indoor pedestrian navigation methods and the inability to rely on active navigation information from the outside during special tasks, this paper proposes an indoor pedestrian navigation method based on multiple constraints. The experimental results show that in the indoor environment of approximately 2600[Formula: see text]m 2 , with a total distance of over 1038.6[Formula: see text]m, the overall recognition rate of the proposed multi-motion mode recognition methods reaches 99%, and the navigation error and RMSE value are less than 4[Formula: see text]m.