Personalized Stride-Length Estimation Based on Active Online Learning

The ability to accurately estimate a user's stride length plays a great important role in various applications. For a new target pedestrian or device, their heterogeneity dramatically reduces the performance of the current stride-length estimation (SLE) methods. To address the issue of heterogeneity, in this article, we propose an SLE method based on a long short-term memory (LSTM) network and denoising autoencoders (DAEs). The LSTM network is used to mine temporal dependencies and extract significant eigenvectors from the corrupted inertial sensor observations. Then, DAEs are adopted to automatically eliminate the inherent noise in eigenvectors and obtain denoised eigenvectors. Finally, a regression module maps the denoised eigenvectors to the resulting stride length. To mitigate the heterogeneity, we propose an unperceived model updating framework based on active online learning to establish a personalized model for a given target pedestrian or device. The proposed framework utilizes a magnetism-aided map-matching approach to automatically generate personalized training data and utilizes online learning technologies to evolve the stride-length model. The extensive experimental results demonstrate that the proposed method outperforms other state-of-the-art algorithms and achieves a promising accuracy with a stride-length error rate of 4.59% at a confidence level of 80%.