Search for: All records

Underwater motion recognition using acoustic wireless networks has a promisingly potential to be applied to the diver activity monitoring and aquatic animal recognition without the burden of expensive underwater cameras which have been used by the image-based underwater classification techniques. However, accurately extracting features that are independent of the complicated underwater environments such as inhomogeneous deep seawater is a serious challenge for underwater motion recognition. Velocities of target body (VTB) during the motion are excellent environment independent features for WiFi-based recognition techniques in the indoor environments, however, VTB features are hard to be extracted accurately in the underwater environments. The inaccurate VTB estimation is caused by the fact that the signal propagates along with a curve instead of a straight line as the signal propagates in the air. In this paper, we propose an underwater motion recognition mechanism in the inhomogeneous deep seawater using acoustic wireless networks. To accurately extract velocities of target body features, we first derive Doppler Frequency Shift (DFS) coefficients that can be utilized for VTB estimation when signals propagate deviously. Secondly, we propose a dynamic self-refining (DSR) optimization algorithm with acoustic wireless networks that consist of multiple transmitter-receiver links to estimate the VTB. Those VTB features can be utilized to train the convolutional neural networks (CNN). Through the simulation, estimated VTB features are evaluated and the testing recognition results validate that our proposed underwater motion recognition mechanism is able to achieve high classification accuracy. 

Driven by the development of machine learning and the development of wireless techniques, lots of research efforts have been spent on the human activity recognition (HAR). Although various deep learning algorithms can achieve high accuracy for recognizing human activities, existing works lack of a theoretical performance upper bound which is the best accuracy that is only limited by the influencing factors in wireless networks such as indoor physical environments and settings of wireless sensing devices regardless of any HAR algorithm. Without the understanding of performance upper bound, mistakenly configuring the influencing factors can reduce the HAR accuracy drastically no matter what deep learning algorithms are utilized. In this paper, we propose the HAR performance upper bound which is the minimum classification error probability that doesn't depend on any HAR algorithms and can be considered as a function of influencing factors in wireless sensing networks for CSI based human activity recognition. Since the performance upper bound can capture the impacts of influencing factors on HAR accuracy, we further analyze the influences of those factors with varying situations such as through the wall HAR and different human activities by MATLAB simulations.