Abstract:
WiFi sensing-based human pose estimation (HPE) has gained significant attention in the academic community due to its advantages over vision-and sensor-based methods, incl...Show MoreMetadata
Abstract:
WiFi sensing-based human pose estimation (HPE) has gained significant attention in the academic community due to its advantages over vision-and sensor-based methods, including non-intrusiveness, convenience, and enhanced privacy protection. However, most existing WiFi-based pose estimators suffer from poor performance and lack robustness in the presence of random noise. To address these challenges, this paper presents a novel HPE architecture comprising two key modules: A Denoiser and an Estimator. The Denoiser is based on an autoencoder structure, while the Estimator is based on a new convolutional neural network (CNN) called SDy-CNN, which is designed to dynamically focus on high-information subcarriers of orthogonal frequency division multiplexing signals. Additionally, Bayesian optimization is employed to fine-tune the architecture’s parameters for optimal performance flexibly. Experiments conducted on a comprehensive dataset, MM-Fi, demonstrate that the proposed architecture significantly outperforms existing state-of-the-art estimators, achieving up to an 8.38% improvement in HPE accuracy in clean data scenarios and up to a 14% improvement in noisy data scenarios. It has also been proven to gain computational efficiency when being much faster than other methods.
Published in: IEEE Internet of Things Journal ( Early Access )