Temporal and spatial variability of the fidgety movement descriptors and their relation to head position in automized general movement assessment

Doroniewicz, Iwona; Ledwoń, Daniel; Danch-Wierzchowska, Marta; Bugdol, Monika; Kieszczyńska, Katarzyna; Affanasowicz, Alicja; Matyja, Małgorzata; Michnik, Robert; Mitas, Andrzej W.; Myśliwiec, Andrzej

doi:10.37190/abb-01839-2021-02

Abstract

Purpose: In clinical practice, motor development in infants is assessed subjectively. Many researchers propose objective methods, which have numerous limitations, by attaching markers or sensors to the child’s limbs. The purpose of this study is to attempt to develop objectified numerical indices to describe the limb movements of infants without interfering with spontaneous activity.

Methods: 20-minute video recordings of three infants’ movements who were purposively selected from 51 subjects were included in the study. The procedure of automatic calculation of head position time in 3 positions was applied. Movement features were determined to allow for the delineation of coefficients describing the movement in numerical values.

Results: Presented parameters describe three infant’s movement aspects: quality (strength), distribution of postural tonus and asymmetry in relation to head position, described as four independent values. Estimated parameters variability over time was weighted up according to expert observations. The presented method is a direct reflection of infants' observation, currently performed by highly educated and experienced therapists.

Conclusions: The interpretability and usefulness of the presented parameters were proved. All parameters estimation is fully automated. The conducted research is a prelude to future work related to creating an objective and repeatable tool, initially monitoring and ultimately supporting early diagnosis for differentiating normal and abnormal motor development.

References

Berthouze L., Margaret M., Design and validation of surface-marker clusters for the quantification of joint rotations in general movements in early infancy, J. Biomech., 2011, 44 (6), DOI: 10.1016/j.jbiomech.2011.01.016.
Search in Google Scholar Back to article
Cao Z., Hidalgo G., Simon T., Wei S., Sheikh Y., Open-Pose: realtime multi-person 2D pose estimation using Part Affinity Fields, IEEE Trans. Pattern Anal. Mach. Intell., 2021, 43 (1), DOI: 10.1109/tpami.2019.2929257.
Search in Google Scholar Back to article
Chen H., Xue M., Mei Z., Bambang Oetomo S., Chen W., A review of wearable sensor systems for monitoring body movements of neonates, Sensors, 2016, 16 (12), 2134, DOI: 10.3390/s16122134.
Search in Google Scholar Back to article
Colyer S.L., Evans M., Cosker D.P., Salo A.I.T., A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system, Sports Medicine – Open, 2018, 4 (1), DOI: 10.1186/s40798-018-0139-y.
Search in Google Scholar Back to article
Doroniewicz I., Ledwoń D., Affanasowicz A., Kieszczyńska K., Latos D., Matyja M., Mitas A.W., Myśliwiec A., Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification, Sensors, 2020, 20 (21), 5986, DOI: 10.3390/s20215986.
Search in Google Scholar Back to article
Doroniewicz I., Ledwoń D., Bugdol M., Kieszczyńska K., Affanasowicz A., Matyja M., Badura D., Mitas A.W., Myśliwiec A., Computer-based analysis of spontaneous infant activity: A pilot study, Information Technology in Biomedicine, 2020, DOI: 10.1007/978-3-030-49666-1_12.
Search in Google Scholar Back to article
Dubowitz L., Ricci D., Mercuri E., The Dubowitz neurological examination of the full-term newborn, Ment. Retard. Dev. Disabil. Res. Rev., 2005, 11 (1), DOI: 10.1002/mrdd.20048.
Search in Google Scholar Back to article
Einspieler C., Peharz R., Marschik P.B., Fidgety movements – tiny in appearance, but huge in impact, J. Pediatr. (Rio J.), 2016, 92 (3), DOI: 10.1016/j.jpedp.2016.03.016.
Search in Google Scholar Back to article
Einspieler C., Prechtl H.F.R., Prechtl’s assessment of general movements: a diagnostic tool for the functional assessment of the young nervous system, Ment. Retard. Dev. Disabil. Res. Rev., 2005, 11 (1), DOI: 10.1002/mrdd.20051.
Search in Google Scholar Back to article
Gajewska E., Sobieska M., Samborski W., Correlates between Munich Functional Development Diagnostics and postural reactivity findings based on seven provovoked postural reactions modus Vojta during the first period of child’s life, Ann. Acad. Med. Stetin., 2006, 52 (3), 67–70.
Search in Google Scholar Back to article
Gilson K.M., Davis E., Reddihough D., Graham K., Waters E., Quality of life in children with cerebral palsy: implications for practice, J. Child Neurol., 2014, 29 (8), DOI: 10.1177/0883073814535502.
Search in Google Scholar Back to article
Groen S.E., de Blécourt A.C., Postema K., Hadders-Algra M., General movements in early infancy predict neuromotor development at 9 to 12 years of age, Dev. Med. Child Neurol., 2005, 47 (11), DOI: 10.1017/s0012162205001544.
Search in Google Scholar Back to article
Groos D., Adde L., Støen R., Ramampiaro H., Ihlen E.A.F., Towards human performance on automatic motion tracking of infant spontaneous movements, arXiv., 2020, ArXiv:2010.05949 [Cs].
Search in Google Scholar Back to article
Hadders-Algra M., Evaluation of motor function in young infants by means of the assessment of general movements: a review, Pediatr. Phys. Ther., 2001, 13 (1), DOI:10.1097/00001577-200104000-00005.
Search in Google Scholar Back to article
Harris S.R., Backman C.L., Mayson T.A., Comparative predictive validity of the Harris Infant Neuromotor Test and the Alberta Infant Motor Scale, Dev. Med. Child Neurol., 2010, 52 (5), DOI: 10.1111/j.1469-8749.2009.03518.x.
Search in Google Scholar Back to article
Ihlen E.A.F., Støen R., Boswell L., de Regnier R.-A., Fjørtoft T., Gaebler-Spira D., Labori C., Loennecken M.C., Msall M.E., Möinichen U.I., Peyton C., Schreiber M.D., Silberg I.E., Songstad N.T., Vågen R.T., Øberg G.K., Adde L., Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study, J. Clin. Med., 2020, 9 (1), DOI: 10.3390/jcm9010005.
Search in Google Scholar Back to article
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 (18), 5321, DOI: 10.3390/s20185321.
Search in Google Scholar Back to article
Johansen K., Persson K., Sonnander K., Magnusson M., Saradi A., Lucas S., Clinical utility of the Structured Observation of Motor Performance in Infants within the child health services, PLoS One, 2017, 12 (7), e0181398, DOI: 10.1371/journal.pone.0181398.
Search in Google Scholar Back to article
Kanemaru N., Watanabe H., Kihara H., Nakano H., Nakamura T., Nakano J., Taga G., Konishi Y., Jerky spontaneous movements at term age in preterm infants who later developed cerebral palsy, Early Hum. Dev., 2014, 90 (8), DOI: 10.1016/j.earlhumdev.2014.05.004.
Search in Google Scholar Back to article
Marchi V., Hakala A., Knight A., D’Acunto F., Scattoni M.L., Guzzetta A., Vanhatalo S., Automated pose estimation captures key aspects of General Movements at eight to 17 weeks from conventional videos, Acta Paediatr., 2019, 108 (10), DOI: 10.1111/apa.14781.
Search in Google Scholar Back to article
Maring J.R., Elbaum L., Concurrent Validity of the Early Intervention Developmental Profile and the Peabody Developmental Motor Scale-2, Pediatr. Phys. Ther., 2007, 19 (2), DOI: 10.1097/pep.0b013e31804a5786.
Search in Google Scholar Back to article
McCay K.D., Ho E.S.L., Shum H.P.H., Fehringer G., Marcroft C., Embleton N.D., Abnormal Infant Movements Classification With Deep Learning on Pose-Based Features, IEEE Access, 2020, 8, DOI: 10.1109/access.2020.2980269.
Search in Google Scholar Back to article
Montgomery C., Hellström-Westas L., Strand-Brodd K., Sonnander K., Persson K., The Structured Observation of Motor Performance in Infants has convergent and discriminant validity in preterm and term infants, Acta Paediatr., 2017, 106 (5), DOI: 10.1111/apa.13774.
Search in Google Scholar Back to article
Nuysink J., Van Haastert I.C., Eijsermans M.J., Koopman-Esseboom C., Helders P.J., de Vries L.S., van der Net J., Prediction of gross motor development and independent walking in infants born very preterm using the Test of Infant Motor Performance and the Alberta Infant Motor Scale, Early Hum. Dev., 2013, 89 (9), DOI: 10.1016/j.earlhumdev.2013.04.016.
Search in Google Scholar Back to article
Piper M.C., Pinnell L.E., Darrah J., Maguire T., Byrne P.J., Construction and validation of the Alberta Infant Motor Scale (AIMS), Can. J. Public Health., 1992, 83, S46–50.
Search in Google Scholar Back to article
Prechtl H.F., General movement assessment as a method of developmental neurology: new paradigms and their consequences: the 1999 Ronnie MacKeith lecture, Dev. Med. Child Neurol., 2001, 43 (12), DOI: 10.1017/s0012162201001529.
Search in Google Scholar Back to article
Prechtl H.F., Einspieler C., Cioni G., Bos A.F., Ferrari F., Sontheimer D., An early marker for neurological deficits after perinatal brain lesions, Lancet, 1997, 349 (9062), DOI: 10.1016/s0140-6736(96)10182-3.
Search in Google Scholar Back to article
Rahmati H., Martens H., Aamo O.M., Stavdahl O., Stoen R., Adde L., Frequency analysis and feature reduction method for prediction of cerebral palsy in young infants, IEEE Trans. Neural. Syst. Rehabil. Eng., 2016, 24 (11), DOI: 10.1109/tnsre.2016.2539390.
Search in Google Scholar Back to article
Rose-Jacobs R., Cabral H., Beeghly M., Brown E.R., Frank D.A., The Movement Assessment of Infants (MAI) as a predictor of two-year neurodevelopmental outcome for infants born at term who are at social risk, Pediatr. Phys. Ther., 2004, 16 (4), DOI: 10.1097/01.pep.0000145931.87152.co.
Search in Google Scholar Back to article
Savitzky A., Golay M.J.E., Smoothing and differentiation of data by simplified least squares procedures, Anal. Chem., 1964, 36 (8), DOI: 10.1021/ac60214a047.
Search in Google Scholar Back to article
Silva N., Zhang D., Kulvicius T., Gail A., Barreiros C., Lindstaedt S., Kraft M., Bölte S., Poutskar L., Nielsen-Saines K., Wörgötter F., Einspieler C., Marschik P.B., The future of General Movement Assessment: The role of computer vision and machine learning – A scoping review, Res. Dev. Disabil., 2021, 110, 103854, DOI: 10.1016/j.ridd.2021.103854.
Search in Google Scholar Back to article
Stahl A., Schellewald C., Stavdahl Ø., Aamo O.M., Adde L., Kirkerød H., An optical flow-based method to predict infantile cerebral palsy, IEEE Trans. Neural. Syst. Rehabil. Eng., 2012, 20 (4), DOI: 10.1109/tnsre.2012.2195030.
Search in Google Scholar Back to article
Szczygieł E., Piotrowski K., Golec J., Czechowska D., Masłoń A., Bac A., Golec E., Head position influence on stabilographic variables, Acta Bioeng. Biomech., 2016, 18 (4), DOI: 10.5277/ABB-00433-2015-02.
Search in Google Scholar Back to article
Tsuji T., Nakashima S., Hayashi H., Soh Z., Furui A., Shibanoki T., Shima K., Shimatani K., Markerless Measurement and evaluation of General Movements in infants, Sci. Rep., 2020, 10 (1), DOI: 10.1038/s41598-020-57580-z.
Search in Google Scholar Back to article

Temporal and spatial variability of the fidgety movement descriptors and their relation to head position in automized general movement assessment

Abstract

Paradigm

My account