Prediction of Personalized Postoperative Implanted Knee Kinematics with Statistical Temporal Modeling

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

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Long-Short Temporal Modeling for Efficient Action Recognition

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp39728.2021.9413807 ◽

2021 ◽

Author(s):

Liyu Wu ◽

Yuexian Zou ◽

Can Zhang

Keyword(s):

Action Recognition ◽

Temporal Modeling

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A patella marker to improve hip and knee kinematics for models with functionally defined joint axes

Gait & Posture ◽

10.1016/j.gaitpost.2021.04.018 ◽

2021 ◽

Vol 87 ◽

pp. 43-48

Author(s):

Andrew J. Ries ◽

Michael H. Schwartz

Keyword(s):

Knee Kinematics

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Pyramidal Framework: Guidance for the Next Generation of GIS Spatial-Temporal Models

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030188 ◽

2021 ◽

Vol 10 (3) ◽

pp. 188

Author(s):

Cyril Carré ◽

Younes Hamdani

Keyword(s):

Conceptual Models ◽

Future Generations ◽

Temporal Data ◽

Temporal Modeling ◽

Temporal Models ◽

Gis Data ◽

Spatio Temporal ◽

New Interpretation ◽

Conventional Literature

Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a demand to further develop spatio-temporal conceptual models to comprehensively represent the nature of the evolution of geographic objects. The latter involves a set of considerations like those related to managing changes and object identities, modeling possible causal relations, and integrating multiple interpretations. While conventional literature generally presents these concepts separately and rarely approaches them from a holistic perspective, they are in fact interrelated. Therefore, we believe that the semantics of modeling would be improved by considering these concepts jointly. In this work, we propose to represent these interrelationships in the form of a hierarchical pyramidal framework and to further explore this set of concepts. The objective of this framework is to provide a guideline to orient the design of future generations of GIS data models, enabling them to achieve a better representation of available spatio-temporal data. In addition, this framework aims at providing keys for a new interpretation and classification of spatio-temporal conceptual models. This work can be beneficial for researchers, students, and developers interested in advanced spatio-temporal modeling.

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Kinematic Comparison between Medially Congruent and Posterior-Stabilized Third-Generation TKA Designs

Journal of Functional Morphology and Kinesiology ◽

10.3390/jfmk6010027 ◽

2021 ◽

Vol 6 (1) ◽

pp. 27

Author(s):

Stefano Ghirardelli ◽

Jessica L. Asay ◽

Erika A. Leonardi ◽

Tommaso Amoroso ◽

Thomas P. Andriacchi ◽

...

Keyword(s):

Knee Flexion ◽

Knee Kinematics ◽

Primary Total Knee Arthroplasty ◽

Flexion Angle ◽

Heel Strike ◽

Third Generation ◽

Knee Flexion Angle ◽

Posterior Stabilized ◽

Rotational Angle ◽

Peak Knee

Background: This study compares knee kinematics in two groups of patients who have undergone primary total knee arthroplasty (TKA) using two different modern designs: medially congruent (MC) and posterior-stabilized (PS). The aim of the study is to demonstrate only minimal differences between the groups. Methods: Ten TKA patients (4 PS, 6 MC) with successful clinical outcomes were evaluated through 3D knee kinematics analysis performed using a multicamera optoelectronic system and a force platform. Extracted kinematic data included knee flexion angle at heel-strike (KFH), peak midstance knee flexion angle (MSKFA), maximum and minimum knee adduction angle (KAA), and knee rotational angle at heel-strike. Data were compared with a group of healthy controls. Results: There were no differences in preferred walking speed between MC and PS groups, but we found consistent differences in knee function. At heel-strike, the knee tended to be more flexed in the PS group compared to the MC group; the MSKFA tended to be higher in the PS group compared to the MC group. There was a significant fluctuation in KAA during the swing phase in the PS group compared to the MC group, PS patients showed a higher peak knee flexion moment compared to MC patients, and the PS group had significantly less peak internal rotation moments than the MC group. Conclusions: Modern, third-generation TKA designs failed to reproduce normal knee kinematics. MC knees tended to reproduce a more natural kinematic pattern at heel-strike and during axial rotation, while PS knees showed better kinematics during mid-flexion.

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