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Longitudinal assessment of U-shaped and inverted U-shaped developmental changes in the spontaneous movements of infants via markerless video analysis.

ABSTRACT: Various attempts have been made to elucidate the development patterns in the spontaneous movements of infants through longitudinal evaluations. Movement complexity has been found to demonstrate u-shaped changes in the measurements focusing on limb movements. However, researchers have not yet clarified how other characteristics, besides movement complexity, change over time. This paper presents a longitudinal evaluation of spontaneous movements in infants using evaluation indices calculated through markerless video analysis. Nine infants with corrected ages from [Formula: see text] to 15 weeks participated in the experiments. We confirmed the change in indices over time using statistical methods. Index changes can be classified as positively correlated, u-shaped, inverted u-shaped, and uncorrelated. We also confirmed that the u-shaped and inverted u-shaped indices are negatively correlated. Furthermore, the principal component analysis revealed that the first principal component had the inverted u-shaped changes with the corrected age. These results suggest that it is important to synchronize the inverted u-shaped variations in the movement and velocity with the u-shaped changes in the movement complexity for infant development.

SUBMITTER: Kinoshita N

PROVIDER: S-EPMC7545215 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Longitudinal assessment of U-shaped and inverted U-shaped developmental changes in the spontaneous movements of infants via markerless video analysis.

Kinoshita Naoki N Furui Akira A Soh Zu Z Hayashi Hideaki H Shibanoki Taro T Mori Hiroki H Shimatani Koji K Funabiki Yasuko Y Tsuji Toshio T

Scientific reports 20201008 1

Various attempts have been made to elucidate the development patterns in the spontaneous movements of infants through longitudinal evaluations. Movement complexity has been found to demonstrate u-shaped changes in the measurements focusing on limb movements. However, researchers have not yet clarified how other characteristics, besides movement complexity, change over time. This paper presents a longitudinal evaluation of spontaneous movements in infants using evaluation indices calculated throu ...[more]

PMID: 33033352

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Project description:ImportanceAmong children, infants and toddlers have some of the highest rates of traumatic brain injury (TBI), but longitudinal information on their developmental outcomes to guide postinjury surveillance is sparse.ObjectiveTo evaluate infants' and toddlers' development over 3 years following TBI compared with those with orthopedic injury (OI).Design, setting, and participantsA longitudinal observational cohort study was conducted at 2 level 1 pediatric trauma centers from January 20, 2013, to September 30, 2015; data analysis was performed from May 12 to October 20, 2021. Participants included children injured when younger than 31 months with TBI or OI who received emergency department care.ExposuresMild, moderate, or severe TBI or OI.Main outcomes and measuresParents completed baseline measures representing preinjury status and 3-, 12-, 24-, and 36-month postinjury status, using the Ages & Stages Questionnaire-3 (ASQ-3), with a mean reference value of 50 and higher scores indicating more advanced development. Linear mixed models characterized children's outcomes for each ASQ-3 domain after adjustment for baseline ASQ-3, injury severity and group, age, injury mechanism (abuse or not), sex, prematurity, family function, social capital, and time. Interactions with time were evaluated.ResultsConsent for participation was provided for 195 children; 184 parents (94%) completed a baseline survey. The cohort included 168 children who completed at least 1 follow-up survey: 48 (29%) mild; 54 (32%) complicated mild/moderate; 21 (13%) severe TBI; and 45 (27%) orthopedic injury. The cohort included 95 boys (57%), 49% injured before age 1 year; and 13% injured by abuse. Mean (SD) age at the time of injury was 13.9 (9.4) months. At 36 months, children with mild or complicated mild/moderate TBI performed similarly to children with OI across ASQ-3 domains. Children with severe TBI performed poorly as shown by negative mean differences in communication (-8.8; 95% CI, -13.8 to -3.8); gross motor (-10.1; 95% CI, -15.1 to -5.1); problem solving (-6.6; 95% CI, -11.2 to -1.9), and personal social (-6.3; 95% CI, -10.4 to -2.1) domains with little recovery over time. Children with abusive injury experienced decrements in ability over time in fine motor and personal social skills. Social capital was protective in communication (mean, 1.5; 95% CI, 0.3-2.7) and problem solving (mean, 1.2; 95% CI, 0.1-2.3) domains.Conclusions and relevanceIn this cohort study of children with TBI, children with severe injury showed little recovery. These findings suggest that early childhood intervention is needed, while children with milder injury remained on their developmental track and should continue routine developmental surveillance.

Project description:Introduction: Video-based clinical rating plays an important role in assessing dystonia and monitoring the effect of treatment in dyskinetic cerebral palsy (CP). However, evaluation by clinicians is time-consuming, and the quality of rating is dependent on experience. The aim of the current study is to provide a proof-of-concept for a machine learning approach to automatically assess scoring of dystonia using 2D stick figures extracted from videos. Model performance was compared to human performance. Methods: A total of 187 video sequences of 34 individuals with dyskinetic CP (8-23 years, all non-ambulatory) were filmed at rest during lying and supported sitting. Videos were scored by three raters according to the Dyskinesia Impairment Scale (DIS) for arm and leg dystonia (normalized scores ranging from 0-1). Coordinates in pixels of the left and right wrist, elbow, shoulder, hip, knee and ankle were extracted using DeepLabCut, an open source toolbox that builds on a pose estimation algorithm. Within a subset, tracking accuracy was assessed for a pretrained human model and for models trained with an increasing number of manually labeled frames. The mean absolute error (MAE) between DeepLabCut's prediction of the position of body points and manual labels was calculated. Subsequently, movement and position features were calculated from extracted body point coordinates. These features were fed into a Random Forest Regressor to train a model to predict the clinical scores. The model performance trained with data from one rater evaluated by MAEs (model-rater) was compared to inter-rater accuracy. Results: A tracking accuracy of 4.5 pixels (approximately 1.5 cm) could be achieved by adding 15-20 manually labeled frames per video. The MAEs for the trained models ranged from 0.21 ± 0.15 for arm dystonia to 0.14 ± 0.10 for leg dystonia (normalized DIS scores). The inter-rater MAEs were 0.21 ± 0.22 and 0.16 ± 0.20, respectively. Conclusion: This proof-of-concept study shows the potential of using stick figures extracted from common videos in a machine learning approach to automatically assess dystonia. Sufficient tracking accuracy can be reached by manually adding labels within 15-20 frames per video. With a relatively small data set, it is possible to train a model that can automatically assess dystonia with a performance comparable to human scoring.

Dataset Information

Longitudinal assessment of U-shaped and inverted U-shaped developmental changes in the spontaneous movements of infants via markerless video analysis.

Publications

Longitudinal assessment of U-shaped and inverted U-shaped developmental changes in the spontaneous movements of infants via markerless video analysis.

Similar Datasets

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