Automating General Movements Assessment with quantitative deep learning to facilitate early screening of cerebral palsy

The Prechtl General Movements Assessment (GMA) is increasingly recognized for its role in evaluating the integrity of the developing nervous system and predicting motor dysfunctions, particularly in conditions such as cerebral palsy (CP). However, the necessity for highly trained professionals has hindered the adoption of GMA as an early screening tool in some countries. In this study, we propose a deep learning-based motor assessment model (MAM) that combines infant videos and basic characteristics, with the aim of automating GMA at the fidgety movements (FMs) stage. MAM demonstrates strong performance, achieving an Area Under the Curve (AUC) of 0.967 during external validation. Importantly, it adheres closely to the principles of GMA and exhibits robust interpretability, as it can accurately identify FMs within videos, showing substantial agreement with expert assessments. Leveraging the predicted FMs frequency, a quantitative GMA method is introduced, which achieves an AUC of 0.956 and enhances the diagnostic accuracy of GMA beginners by 11.0%. The development of MAM holds the potential to significantly streamline early CP screening and revolutionize the field of video-based quantitative medical diagnostics.

Detecting Asymmetry of Upper Limb Activity with Accelerometry in Infants at Risk for Unilateral Spastic Cerebral Palsy

AIMS

To examine whether accelerometry can quantitate asymmetry of upper limb activity in infants aged 3-12 months at risk for developing unilateral spastic cerebral palsy (USCP).

METHOD

A prospective study was performed in 50 infants with unilateral perinatal brain injury at high risk of developing USCP. Triaxial accelerometers were worn on the ipsilateral and contralesional upper limb during the Hand Assessment for Infants (HAI). Infants were grouped in three age intervals (3-5 months, 5-7.5 months and 7.5 until 12 months). Each age interval group was divided in a group with and without asymmetrical hand function based on HAI cutoff values suggestive of USCP.

RESULTS

In a total of 82 assessments, the asymmetry index for mean upper limb activity was higher in infants with asymmetrical hand function compared to infants with symmetrical hand function in all three age groups (ranging from 41 to 51% versus - 2-6%, p < 0.01), while the total activity of both upper limbs did not differ.

CONCLUSIONS

Upper limb accelerometry can identify asymmetrical hand function in the upper limbs in infants with unilateral perinatal brain injury from 3 months onwards and is complementary to the Hand Assessment for Infants.

Wearable accelerometers for measuring and monitoring the motor behaviour of infants with brain damage during CareToy-Revised training

BACKGROUND

Nowadays, wearable sensors are widely used to quantify physical and motor activity during daily life, and they also represent innovative solutions for healthcare. In the clinical framework, the assessment of motor behaviour is entrusted to clinical scales, but they are dependent on operator experience. Thanks to their intrinsic objectivity, sensor data are extremely useful to provide support to clinicians. Moreover, wearable sensors are user-friendly and compliant to be used in an ecological environment (i.e., at home). This paper aims to propose an innovative approach useful to predict clinical assessment scores of infants' motor activity.

MATERIALS AND METHODS

Starting from data acquired by accelerometers placed on infants' wrists and trunk during playtime, we exploit the method of functional data analysis to implement new models combining quantitative data and clinical scales. In particular, acceleration data, transformed into activity indexes and combined with baseline clinical data, represent the input dataset for functional linear models.

CONCLUSIONS

Despite the small number of data samples available, results show correlation between clinical outcome and quantitative predictors, indicating that functional linear models could be able to predict the clinical evaluation. Future works will focus on a more refined and robust application of the proposed method, based on the acquisition of more data for validating the presented models.

TRIAL REGISTRATION NUMBER

ClincalTrials.gov; NCT03211533. Registered: July, 7th 2017. ClincalTrials.gov; NCT03234959. Registered: August, 1st 2017.

Wireless monitoring devices in hospitalized children: a scoping review

The purpose of this study is to provide a structured overview of existing wireless monitoring technologies for hospitalized children. A systematic search of the literature published after 2010 was conducted in Medline, Embase, Scielo, Cochrane, and Web of Science. Two investigators independently reviewed articles to determine eligibility for inclusion. Information on study type, hospital setting, number of participants, use of a reference sensor, type and number of vital signs monitored, duration of monitoring, type of wireless information transfer, and outcomes of the wireless devices was extracted. A descriptive analysis was applied. Of the 1130 studies identified from our search, 42 met eligibility for subsequent analysis. Most included studies were observational studies with sample sizes of 50 or less published between 2019 and 2022. Common problems pertaining to study methodology and outcomes observed were short duration of monitoring, single focus on validity, and lack information on wireless transfer and data management.  Conclusion: Research on the use of wireless monitoring for children in hospitals has been increasing in recent years but often limited by methodological problems. More rigorous studies are necessary to establish the safety and accuracy of novel wireless monitoring devices in hospitalized children. What is Known: • Continuous monitoring of vital signs using wired sensors is the standard of care for hospitalized pediatric patients. However, the use of wires may pose significant challenges to optimal care. What is New: • Interest in wireless monitoring for hospitalized pediatric patients has been rapidly growing in recent years. • However, most devices are in early stages of clinical testing and are limited by inconsistent clinical and technological reporting.

Accelerometer measures in extremely preterm and or extremely low birth weight infants and association with abnormal general movements assessments at 28- and 32-weeks postmenstrual age

BACKGROUND

Assessment of general movements (GMs) in preterm infants is qualitative and potentially subjective. Accelerometers provide quantitative data that could overcome the problems of the GMs assessment.

STUDY AIMS

To determine if quantitative measures (obtained from four tri-axial accelerometers) correlate with GMs assessments performed in the preterm period at 28- or 32-weeks postmenstrual age (PMA).

STUDY DESIGN

Prospective observational study. Tri-axial accelerometers were applied to the dorsum of each hand and foot at 28- and 32-weeks PMA. Simultaneous video recordings of the babies' spontaneous movements were made to assess GMs.

SUBJECTS

Eligible babies were born <28-weeks PMA or had a birth weight of <1000 g. Babies were recruited before they reached 33-weeks PMA.

OUTCOME MEASURES

GMs assessments were made offline on the video recordings. Forty-six quantitative motor parameters were calculated during the same periods of activity and compared with GMs assessments.

RESULTS

At 28-weeks PMA, 24/43 (55.8 %) babies had abnormal GMs. At 32-weeks PMA, 26/57 (45.6 %) had abnormal GMs. The inter-rater reliability of the GMs was poor. When comparing MDS measures between; infants with normal and those with abnormal GMs, at 28-weeks PMA, 7/46 parameters were significantly different, and at 32-weeks PMA, 19/46 parameters were significantly different.

CONCLUSION

Isolated use of quantitative movement measures, obtained from four tri-axial accelerometers before hospital discharge, correlate with the GMs assessments at both 28-weeks and 32-weeks PMA. Accelerometers may provide a useful screening tool for abnormal GMs in preterm infants and could overcome issues with inter-rater reliability.

Automated Movement Analysis to Predict Cerebral Palsy in Very Preterm Infants: An Ambispective Cohort Study

The General Movements Assessment requires extensive training. As an alternative, a novel automated movement analysis was developed and validated in preterm infants. Infants < 31 weeks’ gestational age or birthweight ≤ 1500 g evaluated at 3−5 months using the general movements assessment were included in this ambispective cohort study. The C-statistic, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for a predictive model. A total of 252 participants were included. The median gestational age and birthweight were 274/7 weeks (range 256/7−292/7 weeks) and 960 g (range 769−1215 g), respectively. There were 29 cases of cerebral palsy (11.5%) at 18−24 months, the majority of which (n = 22) were from the retrospective cohort. Mean velocity in the vertical direction, median, standard deviation, and minimum quantity of motion constituted the multivariable model used to predict cerebral palsy. Sensitivity, specificity, positive, and negative predictive values were 55%, 80%, 26%, and 93%, respectively. C-statistic indicated good fit (C = 0.74). A cluster of four variables describing quantity of motion and variability of motion was able to predict cerebral palsy with high specificity and negative predictive value. This technology may be useful for screening purposes in very preterm infants; although, the technology likely requires further validation in preterm and high-risk term populations.

Improving Preterm Infants' Joint Detection in Depth Images Via Dense Convolutional Neural Networks

Preterm infants' spontaneous motility is a valuable diagnostic and prognostic index of motor and cognitive impairments. Despite being recognized as crucial, preterm infant's movement assessment is mostly based on clinicians' visual inspection. The aim of this work is to present a 2D dense convolutional neural network (denseCNN) to detect preterm infant's joints in depth images acquired in neonatal intensive care units. The denseCNN allows to improve the performance of our previous model in the detection of joints and joint connections, reaching a median recall value equal to 0.839. With a view to monitor preterm infants in a scenario where computational resources are scarce, we tested the architecture on a mid-range laptop. The prediction occurs in real-time (0.014 s per image), opening up the possibility of integrating such monitoring system in a domestic environment.

An Automated Approach for General Movement Assessment: A Pilot Study

Objective: The objective of the study was to develop an automatic quantitative approach to identify infants with abnormal movements of the limbs at term equivalent age (TEA) compared with general movement assessment (GMA). Methods: GMA was performed at TEA by a trained operator in neonates with neurological risk. GMs were classified as normal (N) or abnormal (Ab), which included poor repertoire and cramped synchronized movements. The signals from four micro-accelerometers placed on all limbs were recorded for 10 min simultaneously. A global index (KC_index), quantifying the characteristics of individual limb movements and the coordination among the limbs, was obtained by adding normalized kurtosis of the distribution of the first principal component of the acceleration signals to the cross-correlation of the jerk for the upper and lower limbs. Results: Sixty-eight infants were studied. A KC_index cut-off of 201.5 (95% CI: 199.9-205.0) provided specificity = 0.86 and sensitivity = 0.88 in identifying infants with Ab movements. Conclusions: KC_index provides an automatic and quantitative measure that may allow the identification of infants who require further neurological evaluation.

Automated movement recognition to predict motor impairment in high-risk infants: a systematic review of diagnostic test accuracy and meta-analysis

AIM

To assess the sensitivity and specificity of automated movement recognition in predicting motor impairment in high-risk infants.

METHOD

We searched MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, and Scopus databases and identified additional studies from the references of relevant studies. We included studies that evaluated automated movement recognition in high-risk infants to predict motor impairment, including cerebral palsy (CP) and non-CP motor impairments. Two authors independently assessed studies for inclusion, extracted data, and assessed methodological quality using the Quality Assessment of Diagnostic Accuracy Studies-2. Meta-analyses were performed using hierarchical summary receiver operating characteristic models.

RESULTS

Of 6536 articles, 13 articles assessing 59 movement variables in 1248 infants under 5 months corrected age were included. Of these, 143 infants had CP. The overall sensitivity and specificity for motor impairment were 0.73 (95% confidence interval [CI] 0.68-0.77) and 0.70 (95% CI 0.65-0.75) respectively. Comparatively, clinical General Movements Assessment (GMA) was found to have sensitivity and specificity of 98% (95% CI 74-100) and 91% (95% CI 83-93) respectively. Sensor-based technologies had higher specificity (0.88, 95% CI 0.80-0.93).

INTERPRETATION

Automated movement recognition technology remains inferior to clinical GMA. The strength of this study is its meta-analysis to summarize performance, although generalizability of these results is limited by study heterogeneity.

Spontaneous movements in the newborns: a tool of quantitative video analysis of preterm babies

BACKGROUND AND OBJECTIVES

The number of preterm babies is steadily growing world-wide and these neonates are at risk of neuro-motor-cognitive deficits. The observation of spontaneous movements in the first three months of age is known to predict such risk. However, the analysis by specifically trained physiotherapists is not suited for the clinical routine, motivating the development of simple computerized video analysis systems, integrated with a well-structured Biobank to make available for preterm babies a growing service with diagnostic, prognostic and epidemiological purposes.

METHODS

MIMAS (Markerless Infant Movement Analysis System) is a simple, low-cost system of video analysis of spontaneous movements of newborns in their natural environment, based on a single standard RGB camera, without markers attached to the body. The original videos are transformed into binarized sequences highlighting the silhouette of the baby, in order to minimize the illumination effects and increase the robustness of the analysis; such sequences are then coded by a large set of parameters (39) related to the spatial and spectral changes of the silhouette. The parameter vectors of each baby were stored in the Biobank together with related clinical information.

RESULTS

The preliminary test of the system was carried out at the Gaslini Pediatric Hospital in Genoa, where 46 preterm (PT) and 21 full-term (FT) babies (as controls) were recorded at birth (T0) and 8-12 weeks thereafter (T1). A simple statistical analysis of the data showed that the coded parameters are sensitive to the degree of maturation of the newborns (comparing T0 with T1, for both PT and FT babies), and to the conditions at birth (PT vs. FT at T0), whereas this difference tends to vanish at T1. Moreover, the coding method seems also able to detect the few 'abnormal' preterm babies in the PT populations that were analyzed as specific case studies.

CONCLUSIONS

Preliminary results motivate the adoption of this tool in clinical practice allowing for a systematic accumulation of cases in the Biobank, thus for improving the accuracy of data analysis performed by MIMAS and ultimately allowing the adoption of data mining techniques.

AI Approaches Towards Prechtl's Assessment of General Movements: A Systematic Literature Review

General movements (GMs) are spontaneous movements of infants up to five months post-term involving the whole body varying in sequence, speed, and amplitude. The assessment of GMs has shown its importance for identifying infants at risk for neuromotor deficits, especially for the detection of cerebral palsy. As the assessment is based on videos of the infant that are rated by trained professionals, the method is time-consuming and expensive. Therefore, approaches based on Artificial Intelligence have gained significantly increased attention in the last years. In this article, we systematically analyze and discuss the main design features of all existing technological approaches seeking to transfer the Prechtl's assessment of general movements from an individual visual perception to computer-based analysis. After identifying their shared shortcomings, we explain the methodological reasons for their limited practical performance and classification rates. As a conclusion of our literature study, we conceptually propose a methodological solution to the defined problem based on the groundbreaking innovation in the area of Deep Learning.

Analyse der Spontanmotorik im 1. Lebensjahr: Markerlose 3-D-Bewegungserfassung zur Früherkennung von Entwicklungsstörungen

Kinder mit motorischer Entwicklungsstörung profitieren von einer frühen Entwicklungsförderung. Eine frühe Diagnosestellung in der kinderärztlichen Vorsorge (U2–U5) kann durch ein automatisiertes Screening verbessert werden. Bisherige Ansätze einer automatisierten Bewegungsanalyse sind jedoch teuer und aufwendig und nicht in der Breite anwendbar. In diesem Beitrag soll ein neues System zur Videoanalyse, das Kinematic Motion Analysis Tool (KineMAT) vorgestellt werden. Es kann bei Säuglingen angewendet werden und kommt ohne Körpermarker aus. Die Methode wird anhand von 7 Patienten mit unterschiedlichen Diagnosen demonstriert.

Mit einer kommerziell erhältlichen Tiefenbildkamera (RGB-D[Red-Green-Blue-Depth]-Kamera) werden 3‑minütige Videosequenzen von sich spontan bewegenden Säuglingen aufgenommen und mit einem virtuellen Säuglingskörpermodell (SMIL[Skinned Multi-infant Linear]-Modell) in Übereinstimmung gebracht. Das so erzeugte virtuelle Abbild erlaubt es, beliebige Messungen in 3‑D mit hoher Präzision durchzuführen. Eine Auswahl möglicher Bewegungsparameter wird mit diagnosespezifischen Bewegungsauffälligkeiten zusammengeführt.

Der KineMAT und das SMIL-Modell erlauben eine zuverlässige, dreidimensionale Messung der Spontanaktivität bei Säuglingen mit einer sehr niedrigen Fehlerrate. Basierend auf maschinellen Lernalgorithmen kann der KineMAT trainiert werden, pathologische Spontanmotorik automatisiert zu erkennen. Er ist kostengünstig und einfach anzuwenden und soll als Screeninginstrument für die kinderärztliche Vorsorge weiterentwickelt werden.

Development of a Wearable Sensor Network for Quantification of Infant General Movements for the Diagnosis of Cerebral Palsy

Qualitative assessments of infant spontaneous movements can be performed to measure neurodevelopmental status and provide early insight into the presence of any abnormalities. Clinical assessments of infant movements at 12 weeks post term age are up to 98% predictive of the eventual development of Cerebral Palsy, but their reach is often limited to infants already identified as high-risk within the traditional healthcare system. We present the development of a network of wearable sensors designed to noninvasively measure spontaneous movements in infants from 12-20 weeks post-term- age both within the clinic and for future home use. Pilot data on a single healthy term infant is presented to demonstrate clinical functionality towards future validation studies in infants at high-risk of Cerebral Palsy. Using this system for tele- delivered assessments in the home could enhance screening of neurodevelopmental disorders for infants and families in rural and remote areas, a population with reduced health services.

Identification of Developmental Delay in Infants Using Wearable Sensors: Full-Day Leg Movement Statistical Feature Analysis

This paper examines how features extracted from full-day data recorded by wearable sensors are able to differentiate between infants with typical development and those with or at risk for developmental delays. Wearable sensors were used to collect full-day (8-13 h) leg movement data from infants with typical development ([Formula: see text]) and infants at risk for developmental delay ([Formula: see text]). At 24 months, at-risk infants were assessed as having good ([Formula: see text]) or poor ([Formula: see text]) developmental outcomes. With this limited size dataset, our statistical analysis indicated that accelerometer features collected earlier in infancy differentiated between at-risk infants with poor and good outcomes at 24 months, as well as infants with typical development. This paper also tested how these features performed on a subset of the data for which the infant movement was known, i.e., 5-min intervals more representative of clinical observations. Our results on this limited dataset indicated that features for full-day data showed more group differences than similar features for the 5-min intervals, supporting the usefulness of full-day movement monitoring.

Detection of Infant Motor Activity During Spontaneous Kicking Movements for Term and Preterm Infants Using Inertial Sensors

Spontaneous kicking in infants is one of the earliest displays of motor skills. Abnormalities observed in these displays are an important indicator of later abnormal neuromotor function. However, these abnormalities are not well defined and difficult to detect outside of direct clinical observation. To allow for extended, non-clinical observation of spontaneous kicking, IMU sensors are attached to the limb segments of the infant's legs. An activity detection algorithm is then used to quantify kicking activity derived from collected measurement data. This paper presents our method in detail and discusses results from kicking data acquired from term and low-risk preterm infants.

A video/IMU hybrid system for movement estimation in infants

Cerebral palsy is a non-progressive neurological disorder occurring in early childhood affecting body movement and muscle control. Early identification can help improve outcome through therapy-based interventions. Absence of so-called "fidgety movements" is a strong predictor of cerebral palsy. Currently, infant limb movements captured through either video cameras or accelerometers are analyzed to identify fidgety movements. However both modalities have their limitations. Video cameras do not have the high temporal resolution needed to capture subtle movements. Accelerometers have low spatial resolution and capture only relative movement. In order to overcome these limitations, we have developed a system to combine measurements from both camera and sensors to estimate the true underlying motion using extended Kalman filter. The estimated motion achieved 84% classification accuracy in identifying fidgety movements using Support Vector Machine.

Development and feasibility of a wearable infant wrist band for the objective measurement of physical activity using accelerometery

Background

It is important to be able to reliably and feasibly measure infant and toddler physical activity in order to determine adherence to current physical activity guidelines and effects on early life development, growth and health. This study aimed to describe the development of an infant wearable wrist-worn band for the measurement of physical activity; to determine the feasibility of the device data for observational measurement of physical activity and to determine the caregiver reported acceptability of the infant wearable wrist band.

Methods

After various iterations of prototypes and piloting thereof, a final wearable band was designed to fit an Axivity AX3 monitor. Mother and infant/toddler (aged 3–24 months) pairs (n = 152) were recruited, and mothers were asked for their child to wear the band with enclosed monitor at all times for 1 week (minimum 3 days). Feasibility was assessed by determining technical reliability of the data, as well as wear time and compliance according to requirements for observational measurement. Acceptability was assessed via questionnaire.

Results

Technical reliability of the Axivity AX3 monitors in this age group was good. After excluding days that did not have at least 15 h of wear time, only 2% of participants had less than three valid days of data remaining, and 4% of participants had no data (due to device loss or data loss). Therefore, 94% of participants were compliant, having three or more days of wear with at least 15 h of wear per day, thus providing enough valid data for observational measurement. The majority (60%) of mothers reported being “very happy” with the safety of the device, while only 8% were “a little worried”. A large majority (86%) of mothers stated that the band attracted attention from others, although this was mostly attributed to curiosity about the function of the band. Most (80%) of participants rated the comfort of the band as “comfortable”, and 10% rated it as “very comfortable”.

Conclusions

The infant wearable band proved to be feasible and acceptable according to the criteria tested, and compliance wearing the band was good. We have therefore provided a replicable, comfortable and acceptable wearable band for the measurement of infant and toddler physical activity.

Electronic supplementary material

The online version of this article (10.1186/s40814-018-0256-x) contains supplementary material, which is available to authorized users.

Determining if wearable sensors affect infant leg movement frequency

PURPOSE

There is interest in using wearable sensors to measure infant leg movement patterns; however, they were not developed for infant use and their presence may adversely affect infant movement production. Their weight may discourage leg movement production, or their presence may annoy an infant and encourage higher rates of leg movement production. Our purpose was to determine whether wearable sensors affected the frequency of infant leg movements produced.

METHOD

We included 10 infants with typical development and 10 infants at risk of developmental delay, between 2 and 10 months' chronological age.

RESULTS

After collecting and analyzing video recordings of infants, we found a negligible difference between the numbers of spontaneous leg movements made while infants wore sensors, compared to those without sensors.

CONCLUSIONS

Wearable sensors have a negligible effect on the frequency of infant leg movement production, supporting their use in infant movement analysis.

Kinematic characteristics of infant leg movements produced across a full day

Introduction

Our purpose is to directly measure variability in infant leg movement behavior in the natural environment across a full day. We recently created an algorithm to identify an infant-produced leg movement from full-day wearable sensor data from infants with typical development between one and 12 months of age. Here we report the kinematic characteristics of their leg movements produced across a full day.

Methods

Wearable sensor data were collected from 12 infants with typical development for 8–13 h/day. A wearable sensor was attached to each ankle and recorded triaxial accelerometer and gyroscope measurements at 20 Hz. We determined the duration, average acceleration, and peak acceleration of each leg movement and classified its type (unilateral, bilateral synchronous, bilateral asynchronous).

Results

There was a range of leg movement duration (0.23–0.33 s) and acceleration (average 1.59–3.88 m/s², peak 3.10–8.83 m/s²) values produced by infants across visits. Infants predominantly produced unilateral and asynchronous bilateral movements. Our results collected across a full day are generally comparable to kinematic measures obtained by other measurement tools across short periods of time.

Conclusion

Our results describe variable full-day kinematics of leg movements across infancy in a natural environment. These data create a reference standard for the future comparison of infants at risk for developmental delay.

Development of a Wearable Sensor Algorithm to Detect the Quantity and Kinematic Characteristics of Infant Arm Movement Bouts Produced across a Full Day in the Natural Environment

We developed a wearable sensor algorithm to determine the number of arm movement bouts an infant produces across a full day in the natural environment. Full-day infant arm movement was recorded from 33 infants (22 infants with typical development and 11 infants at risk of atypical development) across multiple days and months by placing wearable sensors on each wrist. Twenty second sections of synchronized video data were used to compare the algorithm against visual observation as the gold standard for counting the number of arm movement bouts. Overall, the algorithm counted 173 bouts and the observer identified 180, resulting in a sensitivity of 90%. For each bout produced across the day, we then calculated the following kinematic characteristics: duration, average and peak acceleration, average and peak angular velocity, and type of movement (one arm only, both arms for some portion of the bout, or both arms for the entire bout). As the first step toward developing norms, we present average values of full-day arm movement kinematic characteristics across the first months of infancy for infants with typical development. Identifying and quantifying infant arm movement characteristics produced across a full day has potential application in early identification of developmental delays and the provision of early intervention therapies to support optimal infant development.

A Review of Wearable Sensor Systems for Monitoring Body Movements of Neonates

Characteristics of physical movements are indicative of infants' neuro-motor development and brain dysfunction. For instance, infant seizure, a clinical signal of brain dysfunction, could be identified and predicted by monitoring its physical movements. With the advance of wearable sensor technology, including the miniaturization of sensors, and the increasing broad application of micro- and nanotechnology, and smart fabrics in wearable sensor systems, it is now possible to collect, store, and process multimodal signal data of infant movements in a more efficient, more comfortable, and non-intrusive way. This review aims to depict the state-of-the-art of wearable sensor systems for infant movement monitoring. We also discuss its clinical significance and the aspect of system design.

A scoping review examining physical activity measurement and levels in the first 2 years of life

BACKGROUND

The first few years of life have been identified as a critical stage in the development of activity behaviours, which have been shown to track into later life. This scoping review aims to assess the literature reporting on physical activity levels in the first 2 years of life in order to answer two main questions: (i) how is physical activity measured in this age group? and (ii) how active are infants and toddlers in the first 2 years of life?

METHODS

A search strategy was employed using PubMed with restrictions only on age and language. After applying exclusion criteria, 20 papers were included in the results.

RESULTS

Seventeen studies used some form of objective assessment of physical activity (accelerometers, actometers, direct observation, doubly labelled water or a metabolic chamber); while the remaining three relied on subjective assessments (parent reported questionnaires or interviews, and activity diaries). Nine studies exclusively assessed infants (<12 months), and five exclusively assessed toddlers (>12 months). Only six studies reported physical activity levels and patterns specifically; most included studies measured activity as a covariate or correlate. Therefore, much of the reported data was difficult to assess, as results were vague or incompletely described. Where data were reported sufficiently for analysis, results were equally conflicted regarding whether toddlers were meeting recommended physical activity guidelines.

CONCLUSIONS

This scoping review re-iterates the fact that more studies need to be conducted, which focus primarily on measuring and reporting physical activity levels and patterns in this age group in a comprehensive and standardized way, so that more informed guidelines can be devised and interventions can be designed and implemented where necessary.

Frequency-based features for early cerebral palsy prediction

In this paper we aim at predicting cerebral palsy, the most serious and lifelong motor function disorder in children, at an early age by analysing infants' motion data. An essential step for doing so is to extract informative features with high class separability. We propose a set of features derived from frequency analysis of the motion data. Then, we evaluate the practicality of our features on one of the richest data sets collected to study this disease. In this data set, the motion data are extracted from both electromagnetic sensors as well as video camera. The proposed features are used for classifying both data sets. Using these features, we manage to achieve promising classification performance. Classification accuracy of 91% for the sensor data and 88% for the video-derived data show not only the advantage of employing these features for predicting cerebral palsy, but also that replacing electromagnetic sensors with a video camera is feasible.

Movement recognition technology as a method of assessing spontaneous general movements in high risk infants

Preterm birth is associated with increased risks of neurological and motor impairments such as cerebral palsy. The risks are highest in those born at the lowest gestations. Early identification of those most at risk is challenging meaning that a critical window of opportunity to improve outcomes through therapy-based interventions may be missed. Clinically, the assessment of spontaneous general movements is an important tool, which can be used for the prediction of movement impairments in high risk infants. Movement recognition aims to capture and analyze relevant limb movements through computerized approaches focusing on continuous, objective, and quantitative assessment. Different methods of recording and analyzing infant movements have recently been explored in high risk infants. These range from camera-based solutions to body-worn miniaturized movement sensors used to record continuous time-series data that represent the dynamics of limb movements. Various machine learning methods have been developed and applied to the analysis of the recorded movement data. This analysis has focused on the detection and classification of atypical spontaneous general movements. This article aims to identify recent translational studies using movement recognition technology as a method of assessing movement in high risk infants. The application of this technology within pediatric practice represents a growing area of inter-disciplinary collaboration, which may lead to a greater understanding of the development of the nervous system in infants at high risk of motor impairment.