Markerless Measurement and Evaluation of General Movements in Infants

Abstract

General movements (GMs), a type of spontaneous movement, have been used for the early diagnosis of infant disorders. In clinical practice, GMs are visually assessed by qualified licensees; however, this presents a difficulty in terms of quantitative evaluation. Various measurement systems for the quantitative evaluation of GMs track target markers attached to infants; however, these markers may disturb infants' spontaneous movements. this paper proposes a markerless movement measurement and evaluation system for GMs in infants. The proposed system calculates 25 indices related to GMs, including the magnitude and rhythm of movements, by video analysis, that is, by calculating background subtractions and frame differences. Movement classification is performed based on the clinical definition of GMs by using an artificial neural network with a stochastic structure. This supports the assessment of GMs and early diagnoses of disabilities in infants. in a series of experiments, the proposed system is applied to movement evaluation and classification in full-term infants and lowbirth-weight infants. The experimental results confirm that the average agreement between four GMs classified by the proposed system and those identified by a licensee reaches up to 83.1 ± 1.84%. In addition, the classification accuracy of normal and abnormal movements reaches 90.2 ± 0.94%.

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

“…In addition, the CNN and NN architectures yielded better results compared to the standard machine learning algorithms. Tsuji et al [ 20 ] recorded 21 infants and labeled intervals of 30 s according Prechtl’s assessment by the help of a physical therapist. An Artificial Neural Network with a stochastic structure was trained on the resulting dataset containing 4 classes (WMs: 193; FMs: 279; CS: 31; and PR: 66).…”

“…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.…”

“…They have shown excellent results, however, they lack full automation and also have several fundamental limitations. First, all the studies are either based on a small number of subjects or a fewer number of data samples with respect to CP [ 7 , 18 , 19 , 20 , 25 , 26 , 27 , 32 , 34 ]. It is also not clear if the prediction model in these studies has external validity for high-risk infants.…”

“…Third, the movement features used in previous studies lack generality due to less number of subjects and examples. Lastly, all the reviews, except [ 17 , 20 , 23 , 24 , 35 , 36 ], are not using state-of-the-art Deep Learning (DL) algorithms to automate the GMA process. The DL algorithms are popular approaches of Artificial Intelligence (AI) which not only provide a generalized solution but also perform well for accurate detection of the classes in visual and time-series data.…”

Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification

“…In individual studies, authors have attempted to identify irregularities, mostly by reducing the problem to the classification of the recording into the normal and abnormal groups. The proposed solutions can be divided according to the method of the extraction of features describing the child’s movement into those based on features extracted directly from the recording (optical flow, background subtraction) [ 25 , 26 ] and pose-based features, in which the extraction of features is preceded by the process of locating individual body segments [ 27 , 28 ]. Currently, the extraction capabilities of pose-based features have improved due to the availability of ready-made human pose estimation libraries such as OpenPose [ 29 ], whose accuracy in the case of infant movement analysis has been confirmed in various studies [ 28 , 30 ].…”