Sway analysis and fall prediction method based on spatio-temporal sliding window technique

2016 ◽  
Author(s):  
Kabalan Chaccour ◽  
Hiba Al Assaad ◽  
Amir Hajjam el Hassani ◽  
Rony Darazi ◽  
Emmanuel Andres
Keyword(s):  
Prediction Method ◽  
Sliding Window ◽  
Window Technique ◽  
Fall Prediction ◽  
Spatio Temporal

Overestimation of Low-Dose Radiation in Intensity-Modulated Radiotherapy with Sliding-Window Technique

2009 ◽  
Vol 185 (12) ◽  
pp. 821-829 ◽  
Author(s):  
Hilke Vorwerk ◽  
Daniela Wagner ◽  
Björn Seitz ◽  
Hans Christiansen ◽  
Hendrik A. Wolff ◽  
...  
Keyword(s):  
Low Dose ◽  
Intensity Modulated Radiotherapy ◽  
Sliding Window ◽  
Low Dose Radiation ◽  
Intensity Modulated ◽  
Window Technique ◽  
Dose Radiation

scholarly journals A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform

2021 ◽  
Vol 13 (23) ◽  
pp. 4864
Author(s):  
Langfu Cui ◽  
Qingzhen Zhang ◽  
Liman Yang ◽  
Chenggang Bai
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Prediction Method ◽  
Sliding Window ◽  
Small Sample ◽  
Grey Theory ◽  
Sensing System ◽  
Performance Change ◽  
Sample Data ◽  
Platform System

An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

Human inspired fall prediction method for humanoid robots

2019 ◽  
Vol 121 ◽  
pp. 103257
Author(s):  
Rajesh Subburaman ◽  
Dimitrios Kanoulas ◽  
Luca Muratore ◽  
Nikos G. Tsagarakis ◽  
Jinoh Lee
Keyword(s):  
Prediction Method ◽  
Humanoid Robots ◽  
Fall Prediction
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