Automated postural asymmetry assessment in infants neurodevelopmental evaluation using novel video-based features

Ledwoń, Daniel; Danch–Wierzchowska, Marta; Doroniewicz, Iwona; Kieszczyńska, Katarzyna; Affanasowicz, Alicja; Latos, Dominika; Matyja, Małgorzata; Mitas, Andrzej W.; Myśliwiec, Andrzej

doi:10.1016/j.cmpb.2023.107455

Cited by 2 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous comparable studies made use of classical supervised machine learning approach such as k-Nearest Neighbors, Support Vector Machine, Random Forests, and Quadratic Discriminant Analysis using handcrafted features as well as deep learning approaches. [10][11][12][13] A comparison between HIVE-COTE 2.0 and the use of different algorithms on the current use case is deemed necessary in the future.…”

Section: Discussionmentioning

confidence: 99%

“…Key point detection by markerless motion analysis-also referred to as human pose estimation in the field of computer vision-can be used to process video data as an input for subsequent machine learning or deep learning models. [10][11][12][13][14] Several open-source toolbox codes facilitate key point detection based on convolutional neural networks. 15 Examples of these toolbox codes are OpenPose 16 and DeepLabCut.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Video-Based Methodology for Automated Classification of Dystonia and Choreoathetosis in Dyskinetic Cerebral Palsy During a Lower Extremity Task

Haberfehlner,

Roth,

Vanmechelen

et al. 2024

Neurorehabil Neural Repair

View full text Add to dashboard Cite

Background Movement disorders in children and adolescents with dyskinetic cerebral palsy (CP) are commonly assessed from video recordings, however scoring is time-consuming and expert knowledge is required for an appropriate assessment. Objective To explore a machine learning approach for automated classification of amplitude and duration of distal leg dystonia and choreoathetosis within short video sequences. Methods Available videos of a heel-toe tapping task were preprocessed to optimize key point extraction using markerless motion analysis. Postprocessed key point data were passed to a time series classification ensemble algorithm to classify dystonia and choreoathetosis duration and amplitude classes (scores 0, 1, 2, 3, and 4), respectively. As ground truth clinical scoring of dystonia and choreoathetosis by the Dyskinesia Impairment Scale was used. Multiclass performance metrics as well as metrics for summarized scores: absence (score 0) and presence (score 1-4) were determined. Results Thirty-three participants were included: 29 with dyskinetic CP and 4 typically developing, age 14 years:6 months ± 5 years:15 months. The multiclass accuracy results for dystonia were 77% for duration and 68% for amplitude; for choreoathetosis 30% for duration and 38% for amplitude. The metrics for score 0 versus score 1 to 4 revealed an accuracy of 81% for dystonia duration, 77% for dystonia amplitude, 53% for choreoathetosis duration and amplitude. Conclusions This methodology study yielded encouraging results in distinguishing between presence and absence of dystonia, but not for choreoathetosis. A larger dataset is required for models to accurately represent distinct classes/scores. This study presents a novel methodology of automated assessment of movement disorders solely from video data.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Video-Based Methodology for Automated Classification of Dystonia and Choreoathetosis in Dyskinetic Cerebral Palsy During a Lower Extremity Task

Haberfehlner,

Roth,

Vanmechelen

et al. 2024

Neurorehabil Neural Repair

View full text Add to dashboard Cite

show abstract

Towards novel classification of infants’ movement patterns supported by computerized video analysis

Doroniewicz,

Ledwoń,

Bugdol

et al. 2024

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

Background Positional preferences, asymmetry of body position and movements potentially indicate abnormal clinical conditions in infants. However, a lack of standardized nomenclature hinders accurate assessment and documentation of these preferences over time. Video tools offer a safe and reproducible method to analyze and describe infant movement patterns, aiding in physiotherapy management and goal planning. The study aimed to develop an objective classification system for infant movement patterns with particular emphasis on the specific distribution of muscle tension, using methods of computer analysis of video recordings to enhance accuracy and reproducibility in assessments. Methods The study involved the recording of videos of 51 infants between 6 and 15 weeks of age, born at term, with an Apgar score of at least 8 points. Based on observations of a recording of infant spontaneous movements in the supine position, experts identified postural-motor patterns: symmetry and typical asymmetry linked to the asymmetrical tonic neck reflex. Deviations from the typical postural-motor system were indicated, and subcategories of atypical patterns were distinguished. A computer-based inference system was developed to automatically classify individual patterns. Results The following division of motor patterns was used: (1) normal patterns, including (a) typical (symmetrical, asymmetrical: variants 1 and 2); and (b) atypical (variants: 1 to 4), (2) positional preference, and (3) abnormal patterns. The proposed automatic classification method achieved an expert decision mapping accuracy of 84%. For atypical patterns, the high reproducibility of the system’s results was confirmed. Lower reproducibility, not exceeding 70%, was achieved with typical patterns. Conclusions Based on the observation of infant spontaneous movements, it is possible to identify movement patterns divided into typical and atypical patterns. Computer-based analysis of infant movement patterns makes it possible to objectify and satisfactorily reproduce diagnostic decisions. Supplementary Information The online version contains supplementary material available at 10.1186/s12984-024-01429-3.

show abstract

Automated postural asymmetry assessment in infants neurodevelopmental evaluation using novel video-based features

Cited by 2 publications

References 32 publications

A Novel Video-Based Methodology for Automated Classification of Dystonia and Choreoathetosis in Dyskinetic Cerebral Palsy During a Lower Extremity Task

A Novel Video-Based Methodology for Automated Classification of Dystonia and Choreoathetosis in Dyskinetic Cerebral Palsy During a Lower Extremity Task

Towards novel classification of infants’ movement patterns supported by computerized video analysis

Contact Info

Product

Resources

About