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Review

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

1
Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
2
Punjab University College of Information Technology, University of the Punjab, Lahore 54000, Pakistan
3
Clinic for Pediatric and Adolescent Medicine, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(18), 5321; https://doi.org/10.3390/s20185321
Received: 13 August 2020 / Revised: 14 September 2020 / Accepted: 14 September 2020 / Published: 17 September 2020
(This article belongs to the Special Issue Multimodal Sensing for Understanding Behavior and Personality)
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. View Full-Text
Keywords: general movement assessment; fidgety movements; cerebral palsy; motion sensors; visual sensors; multimodal sensing; physical activity assessment; machine learning; artificial neural network general movement assessment; fidgety movements; cerebral palsy; motion sensors; visual sensors; multimodal sensing; physical activity assessment; machine learning; artificial neural network
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MDPI and ACS Style

Irshad, M.T.; Nisar, M.A.; Gouverneur, P.; Rapp, M.; Grzegorzek, M. AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review. Sensors 2020, 20, 5321. https://doi.org/10.3390/s20185321

AMA Style

Irshad MT, Nisar MA, Gouverneur P, Rapp M, Grzegorzek M. AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review. Sensors. 2020; 20(18):5321. https://doi.org/10.3390/s20185321

Chicago/Turabian Style

Irshad, Muhammad T., Muhammad A. Nisar, Philip Gouverneur, Marion Rapp, and Marcin Grzegorzek. 2020. "AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review" Sensors 20, no. 18: 5321. https://doi.org/10.3390/s20185321

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