Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis

Silvia Orlandi; Kamini Raghuram; Corinna R Smith; David Mansueto; Paige Church; Vibhuti Shah; Maureen Luther; Tom Chau

doi:10.1109/EMBC.2018.8513078

Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis

Silvia Orlandi, Kamini Raghuram, Corinna R Smith, David Mansueto, Paige Church, Vibhuti Shah, Maureen Luther, Tom Chau

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

48 Citations (Scopus)

Abstract

The diagnosis of cerebral palsy (CP) is difficult before 2 years of age. The general movements assessment (GMA) is a method for predicting CP from the spontaneous movements of infants in the first months of life. This assessment has shown high accuracy in predicting CP, but its use is limited by a lack of trained clinicians and its subjective nature. An objective and cost-effective alternative is the automatic videobased assessment of infant movements. Retrospective videos with clinical GMA outcomes were evaluated against eligibility criteria for the automatic analysis consisting of a skin model for segmentation and large displacement optical flow (LDOF) for motion tracking. Kinematic features were extracted to classify the movements as typical or atypical using different classification algorithms. Preliminary classification results obtained from the analysis of 127 videos of preterm infants showed up to 92% of accuracy in predicting CP. A computerbased assessment would provide clinicians with an objective tool for early diagnosis of CP, to facilitate early intervention and improve functional outcomes.

Original language	English
Title of host publication	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publication	Learning from the past, looking to the future
Place of Publication	United States
Publisher	Institute of Electrical and Electronics Engineers
Pages	3598-3601
Number of pages	4
ISBN (Electronic)	978-1-5386-3646-6, 978-1-5386-3645-9
ISBN (Print)	978-1-5386-3647-3
DOIs	https://doi.org/10.1109/EMBC.2018.8513078
Publication status	Published - 28 Oct 2018
Externally published	Yes
Event	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Honolulu, United States Duration: 18 Jul 2018 → 21 Jul 2018

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2018
ISSN (Print)	1557-170X
ISSN (Electronic)	1558-4615

Conference

Conference	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated title	EMBC 2018
Country/Territory	United States
City	Honolulu
Period	18/07/18 → 21/07/18

Keywords

Infant
Video analysis
Cerebral palsy
General movements assessment

Access to Document

10.1109/EMBC.2018.8513078Licence: In Copyright

Cite this

Orlandi, S., Raghuram, K., Smith, C. R., Mansueto, D., Church, P., Shah, V., Luther, M., & Chau, T. (2018). Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Learning from the past, looking to the future (pp. 3598-3601). (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2018). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC.2018.8513078

Orlandi, Silvia ; Raghuram, Kamini ; Smith, Corinna R et al. / Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Learning from the past, looking to the future. United States : Institute of Electrical and Electronics Engineers, 2018. pp. 3598-3601 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis",

abstract = "The diagnosis of cerebral palsy (CP) is difficult before 2 years of age. The general movements assessment (GMA) is a method for predicting CP from the spontaneous movements of infants in the first months of life. This assessment has shown high accuracy in predicting CP, but its use is limited by a lack of trained clinicians and its subjective nature. An objective and cost-effective alternative is the automatic videobased assessment of infant movements. Retrospective videos with clinical GMA outcomes were evaluated against eligibility criteria for the automatic analysis consisting of a skin model for segmentation and large displacement optical flow (LDOF) for motion tracking. Kinematic features were extracted to classify the movements as typical or atypical using different classification algorithms. Preliminary classification results obtained from the analysis of 127 videos of preterm infants showed up to 92% of accuracy in predicting CP. A computerbased assessment would provide clinicians with an objective tool for early diagnosis of CP, to facilitate early intervention and improve functional outcomes.",

keywords = "Infant, Video analysis, Cerebral palsy, General movements assessment",

author = "Silvia Orlandi and Kamini Raghuram and Smith, {Corinna R} and David Mansueto and Paige Church and Vibhuti Shah and Maureen Luther and Tom Chau",

year = "2018",

month = oct,

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doi = "10.1109/EMBC.2018.8513078",

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Orlandi, S, Raghuram, K, Smith, CR, Mansueto, D, Church, P, Shah, V, Luther, M & Chau, T 2018, Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis. in 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Learning from the past, looking to the future. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2018, Institute of Electrical and Electronics Engineers, United States, pp. 3598-3601, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, United States, 18/07/18. https://doi.org/10.1109/EMBC.2018.8513078

Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis. / Orlandi, Silvia; Raghuram, Kamini; Smith, Corinna R et al.
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Learning from the past, looking to the future. United States: Institute of Electrical and Electronics Engineers, 2018. p. 3598-3601 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Orlandi, Silvia

AU - Raghuram, Kamini

AU - Smith, Corinna R

AU - Mansueto, David

AU - Church, Paige

AU - Shah, Vibhuti

AU - Luther, Maureen

AU - Chau, Tom

PY - 2018/10/28

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N2 - The diagnosis of cerebral palsy (CP) is difficult before 2 years of age. The general movements assessment (GMA) is a method for predicting CP from the spontaneous movements of infants in the first months of life. This assessment has shown high accuracy in predicting CP, but its use is limited by a lack of trained clinicians and its subjective nature. An objective and cost-effective alternative is the automatic videobased assessment of infant movements. Retrospective videos with clinical GMA outcomes were evaluated against eligibility criteria for the automatic analysis consisting of a skin model for segmentation and large displacement optical flow (LDOF) for motion tracking. Kinematic features were extracted to classify the movements as typical or atypical using different classification algorithms. Preliminary classification results obtained from the analysis of 127 videos of preterm infants showed up to 92% of accuracy in predicting CP. A computerbased assessment would provide clinicians with an objective tool for early diagnosis of CP, to facilitate early intervention and improve functional outcomes.

AB - The diagnosis of cerebral palsy (CP) is difficult before 2 years of age. The general movements assessment (GMA) is a method for predicting CP from the spontaneous movements of infants in the first months of life. This assessment has shown high accuracy in predicting CP, but its use is limited by a lack of trained clinicians and its subjective nature. An objective and cost-effective alternative is the automatic videobased assessment of infant movements. Retrospective videos with clinical GMA outcomes were evaluated against eligibility criteria for the automatic analysis consisting of a skin model for segmentation and large displacement optical flow (LDOF) for motion tracking. Kinematic features were extracted to classify the movements as typical or atypical using different classification algorithms. Preliminary classification results obtained from the analysis of 127 videos of preterm infants showed up to 92% of accuracy in predicting CP. A computerbased assessment would provide clinicians with an objective tool for early diagnosis of CP, to facilitate early intervention and improve functional outcomes.

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ER -

Orlandi S, Raghuram K, Smith CR, Mansueto D, Church P, Shah V et al. Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Learning from the past, looking to the future. United States: Institute of Electrical and Electronics Engineers. 2018. p. 3598-3601. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2018.8513078

Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis

Abstract

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Keywords

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