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Motion

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Estimating heading from optic flow: Comparing deep learning network and human performance.

Neural networks : the official journal of the International Neural Network Society
Convolutional neural networks (CNNs) have made significant advances over the past decade with visual recognition, matching or exceeding human performance on certain tasks. Visual recognition is subserved by the ventral stream of the visual system, wh...

Design of Moving Target Detection System Using Lightweight Deep Learning Model and Its Impact on the Development of Sports Industry.

Computational intelligence and neuroscience
The intelligent tracking and detection of athletes' actions and the improvement of action standardization are of great practical significance to reducing the injury caused by sports in the sports industry. For the problems of nonstandard movement and...

Milli-scale cellular robots that can reconfigure morphologies and behaviors simultaneously.

Nature communications
Modular robot that can reconfigure architectures and functions has advantages in unpredicted environment and task. However, the construction of modular robot at small-scale remains a challenge since the lack of reliable docking and detaching strategi...

QMEDNet: A quaternion-based multi-order differential encoder-decoder model for 3D human motion prediction.

Neural networks : the official journal of the International Neural Network Society
In order to deal with the sequence information in the task of 3D human motion prediction effectively, many previous methods seek to predict the motion state of the next moment using the traditional recurrent neural network in Euclidean space. However...

Development of Smartphone Application for Markerless Three-Dimensional Motion Capture Based on Deep Learning Model.

Sensors (Basel, Switzerland)
To quantitatively assess pathological gait, we developed a novel smartphone application for full-body human motion tracking in real time from markerless video-based images using a smartphone monocular camera and deep learning. As training data for de...

Application of Improved VMD-LSTM Model in Sports Artificial Intelligence.

Computational intelligence and neuroscience
In recent years, with the rapid development of a new generation of artificial intelligence technology, how to deeply apply artificial intelligence technology to physical education and break through the limitations of time-space scenarios and knowledg...

Fully body visual self-modeling of robot morphologies.

Science robotics
Internal computational models of physical bodies are fundamental to the ability of robots and animals alike to plan and control their actions. These "self-models" allow robots to consider outcomes of multiple possible future actions without trying th...

Path Planning for Wheeled Mobile Robot in Partially Known Uneven Terrain.

Sensors (Basel, Switzerland)
Path planning for wheeled mobile robots on partially known uneven terrain is an open challenge since robot motions can be strongly influenced by terrain with incomplete environmental information such as locally detected obstacles and impassable terra...

Human Sports Action and Ideological and PoliticalEvaluation by Lightweight Deep Learning Model.

Computational intelligence and neuroscience
The purpose is to automatically and quickly analyze whether the rope skipping actions conform to the standards and give correct guidance and training plans. Firstly, aiming at the problem of motion analysis, a deep learning (DL) framework is proposed...

Real time volumetric MRI for 3D motion tracking via geometry-informed deep learning.

Medical physics
PURPOSE: To develop a geometry-informed deep learning framework for volumetric MRI with sub-second acquisition time in support of 3D motion tracking, which is highly desirable for improved radiotherapy precision but hindered by the long image acquisi...