Image-Assisted Discrimination Method for Neurodevelopmental Disorders in Infants Based on Multi-feature Fusion and Ensemble Learning

Dai, Xiaohui; Wang, Shigang; Li, Honghua; Yue, Haichun; Min, Jiayuan

doi:10.1007/978-3-030-37078-7_11

Cited by 4 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, marker-free approaches provide the freedom of using no markers, so they are inexpensive and easy to setup. We have presented several approaches in Section 5.2.2 that use marker-free pose estimation for GMA [ 21 , 89 ]. Most of the approaches employed hand-crafted features [ 16 , 22 ] to quantify the amount of motion for the classification of fidgety and non-fidgety movements.…”

Section: Discussionmentioning

confidence: 99%

“…However, the features were sensitive to lightening conditions, cloths, and skin color [ 14 ]. Some approaches have used the trajectory of limbs [ 21 , 33 ] to identify the normal and abnormal movements. The authors in [ 13 ] worked on a large dataset and presented results using frequency and time-based features.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the proposed data analysis methods [ 14 , 16 ] are sensitive to lighting conditions, cloths, and skin color. Dai et al [ 21 ] evaluated the use of a Kernel Correlation Filter (KCF) [ 91 ] to track trajectories of the limbs and whole body of infants to classify their movement as normal or abnormal. Motion trajectories were split in their X and Y components and the X axis discarded for later computations.…”

Section: Methodology Of the Reviewed Approachesmentioning

confidence: 99%

“…In the majority voting scheme, each model gives one vote (i.e., label), and the majority label in the composition is selected as the final decision. It is worthwhile to mention that the reviewed GMA studies [ 13 , 21 , 23 , 24 ] used the decision fusion with motion sensors data or visual sensors data.…”

Section: Methodology Of the Reviewed Approachesmentioning

confidence: 99%

“…As a result of the nominal use of GMA in neonatal follow-up programs, several studies have tried to automate this method. These studies are based on either indirect sensing using visual sensors (2D or 3D video) [ 7 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 24 ], direct sensing using motion sensors [ 25 , 26 , 27 , 28 , 29 , 30 , 31 ], or both [ 32 , 33 , 34 ]. They have shown excellent results, however, they lack full automation and also have several fundamental limitations.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

Irshad

Nisar

Gouverneur

et al. 2020

Sensors

View full text Add to dashboard Cite

General movements (GMs) are spontaneous movements of infants up to five months post-term involving the whole body varying in sequence, speed, and amplitude. The assessment of GMs has shown its importance for identifying infants at risk for neuromotor deficits, especially for the detection of cerebral palsy. As the assessment is based on videos of the infant that are rated by trained professionals, the method is time-consuming and expensive. Therefore, approaches based on Artificial Intelligence have gained significantly increased attention in the last years. In this article, we systematically analyze and discuss the main design features of all existing technological approaches seeking to transfer the Prechtl’s assessment of general movements from an individual visual perception to computer-based analysis. After identifying their shared shortcomings, we explain the methodological reasons for their limited practical performance and classification rates. As a conclusion of our literature study, we conceptually propose a methodological solution to the defined problem based on the groundbreaking innovation in the area of Deep Learning.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodology Of the Reviewed Approachesmentioning

confidence: 99%

Section: Methodology Of the Reviewed Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

Irshad

Nisar

Gouverneur

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

Novel AI driven approach to classify infant motor functions

Reich

Zhang

Kulvičius

et al. 2021

Sci Rep

View full text Add to dashboard Cite

The past decade has evinced a boom of computer-based approaches to aid movement assessment in early infancy. Increasing interests have been dedicated to develop AI driven approaches to complement the classic Prechtl general movements assessment (GMA). This study proposes a novel machine learning algorithm to detect an age-specific movement pattern, the fidgety movements (FMs), in a prospectively collected sample of typically developing infants. Participants were recorded using a passive, single camera RGB video stream. The dataset of 2800 five-second snippets was annotated by two well-trained and experienced GMA assessors, with excellent inter- and intra-rater reliabilities. Using OpenPose, the infant full pose was recovered from the video stream in the form of a 25-points skeleton. This skeleton was used as input vector for a shallow multilayer neural network (SMNN). An ablation study was performed to justify the network’s architecture and hyperparameters. We show for the first time that the SMNN is sufficient to discriminate fidgety from non-fidgety movements in a sample of age-specific typical movements with a classification accuracy of 88%. The computer-based solutions will complement original GMA to consistently perform accurate and efficient screening and diagnosis that may become universally accessible in daily clinical practice in the future.

show abstract

Automatically Measure the Quality of Infants’ Spontaneous Movement via Videos to Predict the Risk of Cerebral Palsy

Xu²,

Wang

et al. 2021

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

Image-Assisted Discrimination Method for Neurodevelopmental Disorders in Infants Based on Multi-feature Fusion and Ensemble Learning

Cited by 4 publications

References 24 publications

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

Novel AI driven approach to classify infant motor functions

Automatically Measure the Quality of Infants’ Spontaneous Movement via Videos to Predict the Risk of Cerebral Palsy

Contact Info

Product

Resources

About