TY - JOUR
T1 - AI approaches towards prechtl’s assessment of general movements: A systematic literature review
AU - Irshad, Muhammad Tausif
AU - Nisar, Muhammad Adeel
AU - Gouverneur, Philip
AU - Rapp, Marion
AU - Grzegorzek, Marcin
N1 - Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9/2
Y1 - 2020/9/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85091045686&partnerID=8YFLogxK
U2 - 10.3390/s20185321
DO - 10.3390/s20185321
M3 - Scientific review articles
C2 - 32957598
AN - SCOPUS:85091045686
SN - 1424-8220
VL - 20
SP - 1
EP - 32
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 18
M1 - 5321
ER -