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This study aims to design an optimized algorithm for low-cost pedestrian navigation system (PNS) to correct the heading drift and altitude error, thus achieving…
This study aims to design an optimized algorithm for low-cost pedestrian navigation system (PNS) to correct the heading drift and altitude error, thus achieving high-precise pedestrian location in both two-dimensional (2-D) and three-dimensional (3-D) space.
A novel heading correction algorithm based on smoothing filter at the terminal of zero velocity interval (ZVI) is proposed in the paper. This algorithm adopts the magnetic sensor to calculate all the heading angles in the ZVI and then applies a smoothing filter to obtain the optimal heading angle. Furthermore, heading correction is executed at the terminal moment of ZVI. Meanwhile, an altitude correction algorithm based on step height constraint is proposed to suppress the altitude channel divergence of strapdown inertial navigation system by using the step height as the measurement of the Kalman filter.
The verification experiments were carried out in 2-D and 3-D space to evaluate the performance of the proposed pedestrian navigation algorithm. The results show that the heading drift and altitude error were well corrected. Meanwhile, the path calculated by the novel algorithm has a higher match degree with the reference trajectory, and the positioning errors of the 2-D and 3-D trajectories are both less than 0.5 per cent.
Besides zero velocity update, another two problems, namely, heading drift and altitude error in the PNS, are solved, which ensures the high positioning precision of pedestrian in indoor and outdoor environments.
This study focuses on perceived overload from environmental stimuli and individual psychology and behavioral interactions. It constructs a theoretical model with overload…
This study focuses on perceived overload from environmental stimuli and individual psychology and behavioral interactions. It constructs a theoretical model with overload as the key stressor based on the stressor-strain-outcome (SSO) model. The authors argue that system feature overload (SFO), information overload, and social overload lead to two psychological strains: fear of missing out (FoMO) and fatigue among users of short video platforms, affecting their discontinuous usage intentions.
To test the hypotheses, the authors conducted a questionnaire survey on 412 users' short video platform usage and empirically tested the constructed model using the research tool SmartPLS 3.3.2.
The results of data analysis showed that most of the hypotheses were supported. Specifically, system feature overload, information overload and social overload all positively affected FoMO. However, SFO and information overload significantly affected fatigue. There was no significant relationship between social overload and fatigue. In addition, both FoMO and fatigue negatively influenced users' discontinuous usage intentions.
The current research on user behavior in information systems tends to focus on the influence in the positive direction and less on the negative direction. The research on discontinuous usage intention (DUI) is a very new research topic. This research studies the influencing factors of users' discontinuous behavior from the perspective of perceptual overload. It provides a unique view for future short video platform user behavior research, with significant theoretical contributions and essential practice for short video platform operators to improve services.