Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network

Jalata, Ibsa; Truong, Thanh-Dat; Allen, Jessica L.; Seo, Han‐Seok; Luu, Khoa

doi:10.3390/fi13080194

Cited by 17 publications

(4 citation statements)

References 41 publications

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“…The GCNN is a type of deep learning model used for processing graph-structured data (Jalata et al, 2021). Traditional deep learning models are mainly suitable for regular structured data, while GCNN is designed specifically for non-regular structured graph data, such as social networks, recommendation systems, and power systems.…”

Section: Gcnn Modelmentioning

confidence: 99%

Research on smart grid management and security guarantee of sports stadiums based on GCNN-GRU and self-attention mechanism

2023

Front. Energy Res.

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Introduction: Smart grid management and security in sports stadiums have gained global attention as significant topics in the field of deep learning. This paper proposes a method based on the Graph Convolutional Neural Network (GCNN) with Gated Recurrent Units (GRU) and a self-attention mechanism. The objective is to predict trends and influencing factors in smart grid management and security of sports stadiums, facilitating the formulation of optimization strategies and policies.Methods: The proposed method involves several steps. Firstly, historical data of sports stadium grid management and security undergo preprocessing using the GCNN and GRU networks to extract time series information. Then, the GCNN is utilized to analyze smart grid data of sports stadiums. The model captures spatial correlations and temporal dynamics, while the self-attention mechanism enhances focus on relevant information.Results and discussion: The experimental results demonstrate that the proposed method, based on GCNN-GRU and the self-attention mechanism, effectively addresses the challenges of smart grid management and security in sports stadiums. It accurately predicts trends and influencing factors in smart grid management and security, facilitating the formulation of optimization strategies and policies. These results also demonstrate that our method has achieved outstanding performance in the image generation task and exhibits strong adaptability across different datasets.

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Section: Gcnn Modelmentioning

confidence: 99%

Research on smart grid management and security guarantee of sports stadiums based on GCNN-GRU and self-attention mechanism

2023

Front. Energy Res.

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“…Object detection has been getting attention in the computer vision community because of the rapid growth of deep learning and its importance for different applications, including security, robotics, autonomous driving, [1][2][3][4][5] image deblurring 6,7 and others. The quality of data annotation and the quantity of data play an important role in building an object detection model.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised learning for fish species recognition

Alaba

Shah

Nabi³

et al. 2023

Ocean Sensing and Monitoring XV

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Fish species recognition and detection are essential for fishery industries. Accurate and robust species classification and detection play a vital role in monitoring fish activities and identifying the distribution of a specific species, which is vital to know the endangered species. It is also essential for controlling production and overall ecosystem control and management. However, the role of current artificial intelligence technologies, such as deep learning, is limited in the ocean system compared to other areas like robotics and security. The major challenge in building a deep learning network is data availability, time, and cost of annotation and labeling. In this work, we build a semi-supervised deep-learning network to recognize fish species. The model is based on a student-teacher network where the teacher network generates pseudo-labels, and the student network is trained with the generated pseud-labels and the labeled data simultaneously. The student network updates the teacher network via an exponential moving average method. The model consists of a faster R-CNN with a feature pyramid network detector. The experimental result of the model on the challenging fish dataset shows a promising result for building semi-supervised object detection models.

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“…The methods used by the researchers included the Convolutional Neural Network (CNN), RF, and Ridge Regression (RR)-they were trained using ordinary videos of patients with CP. Another similar work was conducted by Jalata et al [59]. They proposed a Graph Convolutional Neural Network (GCNN) that predicts similar gait measures based on a video of a patient.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Gross Motor Functions in Children with Cerebral Palsy Using Zebris FDM-T Treadmill

Bedla

Pięta

Kaczmarski

et al. 2022

JCM

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A standardized observational instrument designed to measure change in gross motor function over time in children with cerebral palsy is the Gross Motor Function Measure (GMFM). The process of evaluating a value for the GMFM index can be time consuming. It typically takes 45 to 60 min for the patient to complete all tasks, sometimes in two or more sessions. The diagnostic procedure requires trained and specialized therapists. The paper presents the estimation of the GMFM measure for patients with cerebral palsy based on the results of the Zebris FDM-T treadmill. For this purpose, the regression analysis was used. Estimations based on the Generalized Linear Regression were assessed using different error metrics. The results obtained showed that the GMFM score can be estimated with acceptable accuracy. Because the Zebris FDM-T is a widely used device in gait rehabilitation, our method has the potential to be widely adopted for objective diagnostics of children with cerebral palsy.

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Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network

Cited by 17 publications

References 41 publications

Research on smart grid management and security guarantee of sports stadiums based on GCNN-GRU and self-attention mechanism

Research on smart grid management and security guarantee of sports stadiums based on GCNN-GRU and self-attention mechanism

Semi-supervised learning for fish species recognition

Estimation of Gross Motor Functions in Children with Cerebral Palsy Using Zebris FDM-T Treadmill

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