Toward Predicting Infant Developmental Outcomes From Day-Long Inertial Motion Recordings

Fitter, Naomi T.; Funke, Rebecca; Pulido, José Carlos; Matarić, Maja J.; Smith, Beth A.

doi:10.1109/tnsre.2020.3016916

Cited by 3 publications

(3 citation statements)

References 41 publications

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“…The gestational age (GA) prediction process is an important health prediction method for the infant that predicts accurate details about the health condition. GA also provides solutions to cure the health conditions of infants that reduce their critical condition of infants [ 9 ]. The reduction of infant weight is also a cause of sickness identified by using sports data.…”

Section: Introductionmentioning

confidence: 99%

Health Information Prediction System of Infant Sports Based on Deep Learning Network

Zhang

2022

BioMed Research International

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The sensed data from infant sports and training programs are useful in analyzing their health conditions and forecasting any disorders or abnormalities. The sensed information is processed for providing errorless predictions for infant diseases/disorders, coupled with artificial intelligence and sophisticated healthcare technologies. The problem of noncongruent sensed data impacting the forecast occurs due to errors between consecutive training iterations. This problem is addressed using the deep learning (PEST-DL) proposed perceptible error segregation technique. The training process is halted between two consecutive iterations generating errors until a similarity verification based on infant history is performed. The similarity output determines the errors due to mismatching data observations, and therefore, the data augmentation is performed. The first perceptible error is mitigated by training the learning paradigm with all possible infant history data in the learning process. This prevents prediction lag and data omissions due to discrete availability. The learning is trained from the identified error with the precise detected disorder/abnormality data previously detected. Therefore, the first and consecutive training data segregate error instances from the actual training iterations. This improves the prediction accuracy and precision with controlled error and time complexity.

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Section: Introductionmentioning

confidence: 99%

Health Information Prediction System of Infant Sports Based on Deep Learning Network

Zhang

2022

BioMed Research International

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“…Early and accurate identification of differing developmental trajectories is a vital next step in understanding these emerging phenotypes and supporting optimal outcomes. Published work by Smith and colleagues reports on the utility of wearable sensors to accurately capture infant's limb movement data21,22,24 and the use of that data as an early predictor of developmental disability in AR infants 17,28. Further development of these technologies in infants with SMA would add unique and complementary information to our clinical assessments, improving our understanding of motor development and outcomes in presymptomatic treated infants with SMA.…”

Section: Contribution To Evidence-based Practicementioning

confidence: 99%

“…How does this study fill this gap? Published work reports on the utility of wearable sensors to accurately capture infant's limb movements21,22,24 and their use as an early predictor of developmental disability 17,28. This is an initial step in establishing the utility of wearable sensors to monitor motor function and development in infants with SMA.…”

Section: Introductionmentioning

confidence: 99%

Daily Quantity and Kinematic Characteristics of Leg Movement in a Child With SMA (2 Copies SMN2)

McIntyre,

Dunn,

David

et al. 2023

Pediatric Physical Therapy

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Purpose: This case report describes daily leg movement quantity and kinematic characteristics of a child with spinal muscular atrophy (SMA) with 2 copies of SMN2, who was symptomatic at the time of treatment with disease-modifying therapies. Key Points: Compared with infants with typical development, this child had differing values for leg movement quantity, duration, average acceleration, and peak acceleration measured across full days in the natural environment by wearable sensors. In addition, movement quantity and clinician-rated outcomes increased with age. Conclusions: Wearable sensors recorded movement quantity and kinematic characteristics in a treated infant with SMA (2 copies SMN2). These movement parameters were consistently different compared anecdotally with published data from infants with typical development, demonstrating their potential to add unique and complementary information to the assessment of motor function in SMA. Recommendations: Larger longitudinal studies are needed to determine the utility of wearable sensors as an assessment tool and an early predictor of motor outcomes in children with SMA.

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Artificial intelligence in child development monitoring: A systematic review on usage, outcomes and acceptance

Reinhart,

Bischops,

Kerth

et al. 2024

Intelligence-Based Medicine

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Toward Predicting Infant Developmental Outcomes From Day-Long Inertial Motion Recordings

Cited by 3 publications

References 41 publications

Health Information Prediction System of Infant Sports Based on Deep Learning Network

Health Information Prediction System of Infant Sports Based on Deep Learning Network

Daily Quantity and Kinematic Characteristics of Leg Movement in a Child With SMA (2 Copies SMN2)

Artificial intelligence in child development monitoring: A systematic review on usage, outcomes and acceptance

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