Kinematic characteristics of infant leg movements produced across a full day

Barcoding, linear and nonlinear analysis of full-day leg movements in infants with typical development and infants at risk of developmental disabilities: Cross-sectional study

Traditional methods do not capture the multidimensional domains and dynamic nature of infant behavioral patterns. We aim to compare full-day, in-home leg movement data between infants with typical development (TD) and infants at risk of developmental disabilities (AR) using barcoding and nonlinear analysis. Eleven infants with TD (2-10 months) and nine infants AR (adjusted age: 2-14 months) wore a sensor on each ankle for 7 days. We calculated the standard deviation for linear variability and sample entropy (SampEn) of leg acceleration and angular velocity for nonlinear variability. Movements were also categorized into 16 barcoding states, and we calculated the SampEn and proportions of the barcoding. All variables were compared between the two groups using independent-samples t-test or Mann-Whitney U test. The AR group had larger linear variability compared to the TD group. SampEn was lower in the AR group compared to TD group for both acceleration and angular velocity. Two barcoding states' proportions were significantly different between the two groups. The results showed that nonlinear analysis and barcoding could be used to identify the difference of dynamic multidimensional movement patterns between infants AR and infants with TD. This information may help early diagnosis of developmental disabilities in the future.

Motor Overflow during Reaching in Infancy: Quantification of Limb Movement Using Inertial Motion Units

Early in life, infants exhibit motor overflow, which can be defined as the generation of involuntary movements accompanying purposeful actions. We present the results of a quantitative study exploring motor overflow in 4-month-old infants. This is the first study quantifying motor overflow with high accuracy and precision provided by Inertial Motion Units. The study aimed to investigate the motor activity across the non-acting limbs during goal-directed action. To this end, we used wearable motion trackers to measure infant motor activity during a baby-gym task designed to capture overflow during reaching movements. The analysis was conducted on the subsample of participants (n = 20), who performed at least four reaches during the task. A series of Granger causality tests revealed that the activity differed depending on the non-acting limb and the type of the reaching movement. Importantly, on average, the non-acting arm preceded the activation of the acting arm. In contrast, the activity of the acting arm was followed by the activation of the legs. This may be caused by their distinct purposes in supporting postural stability and efficiency of movement execution. Finally, our findings demonstrate the utility of wearable motion trackers for precise measurement of infant movement dynamics.

Locomotor learning in infants at high risk for cerebral palsy: A study protocol

Background

Physical disability in individuals with cerebral palsy (CP) creates lifelong mobility challenges and healthcare costs. Despite this, very little is known about how infants at high risk for CP learn to move and acquire early locomotor skills, which set the foundation for lifelong mobility. The objective of this project is to characterize the evolution of locomotor learning over the first 18 months of life in infants at high risk for CP. To characterize how locomotor skill is learned, we will use robotic and sensor technology to provide intervention and longitudinally study infant movement across three stages of the development of human motor control: early spontaneous movement, prone locomotion (crawling), and upright locomotion (walking).

Study design

This longitudinal observational/intervention cohort study (ClinicalTrials.gov Identifier: NCT04561232) will enroll sixty participants who are at risk for CP due to a brain injury by one month post-term age. Study participation will be completed by 18 months of age. Early spontaneous leg movements will be measured monthly from 1 to 4 months of age using inertial sensors worn on the ankles for two full days each month. Infants who remain at high risk for CP at 4 months of age, as determined from clinical assessments of motor function and movement quality, will continue through two locomotor training phases. Prone locomotor training will be delivered from 5 to 9 months of age using a robotic crawl training device that responds to infant behavior in real-time. Upright locomotor training will be delivered from 9 to 18 months of age using a dynamic weight support system to allow participants to practice skills beyond their current level of function. Repeated assessments of locomotor skill, training characteristics (such as movement error, variability, movement time and postural control), and variables that may mediate locomotor learning will be collected every two months during prone training and every three months during upright training.

Discussion

This study will develop predictive models of locomotor skill acquisition over time. We hypothesize that experiencing and correcting movement errors is critical to skill acquisition in infants at risk for CP and that locomotor learning is mediated by neurobehavioral factors outside of training.Project Number 1R01HD098364-01A1.ClinicalTrials.gov Identifier: NCT04561232.

Automated assessment of infant motor development to predict infant age: The determination of objective metrics of spontaneous kicking

Though early intervention can improve outcomes for children with motor disabilities, delays in diagnosis can impact the success of intervention programs. Prior work indicates that spontaneous kicking patterns can be used to model typical infant motor development to assist in the early detection of motor delays. However, abnormalities in spontaneous movements are not well defined or readily observable through traditional functional assessments. In this research, a method is introduced for the early detection of delays through the assessment of spontaneous kicking data gathered using a wearable sensing suit. We present formulations of kinematic features identified in the clinical space, identify which features are significant predictors of infant age, and establish normative values. Finally, we offer an analysis of preterm (PT) infant data compared to normative values derived from term infants. Term and PT infants ranging in age from 1 to 10 months were studied. We found that frequency, duration, acceleration, inter-joint coordination, and maximum joint excursion metrics had a significant correlation with age. From these features, models of typical kicking development were created using data from term, typically developing infants. When compared to normative trends, PT infants display differing developmental trends.

Design of pediatric robot to simulate infant biomechanics for neuro-developmental assessment in a sensorized gym

Infants at risk for developmental delays often exhibit postures and movements that may provide a window into potential impairment for cerebral palsy and other neuromotor conditions. We developed a simple 4 DOF robot pediatric simulator to help provide insight into how infant kinematic movements may affect the center of pressure (COP), a common measure thought to be sensitive to neuromotor delay when assessed from supine infants at play. We conducted two experiments: 1) we compared changes in COP caused by limb movements to a human infant and 2) we determined if we could predict COP position due to limb movements using simulator kinematic pose retrieved from video and a sensorized mat. Our results indicate that the limb movements alone were not sufficient to mimic the COP in a human infant. In addition, we show that given a robot simulator and a simple camera, we can predict COP measured by a force sensing mat. Future directions suggest a more complex robot is needed such as one that may include trunk DOF.

Changes in the Complexity of Limb Movements during the First Year of Life across Different Tasks

Infants’ limb movements evolve from disorganized to more selectively coordinated during the first year of life as they learn to navigate and interact with an ever-changing environment more efficiently. However, how these coordination patterns change during the first year of life and across different contexts is unknown. Here, we used wearable motion trackers to study the developmental changes in the complexity of limb movements (arms and legs) at 4, 6, 9 and 12 months of age in two different tasks: rhythmic rattle-shaking and free play. We applied Multidimensional Recurrence Quantification Analysis (MdRQA) to capture the nonlinear changes in infants’ limb complexity. We show that the MdRQA parameters (entropy, recurrence rate and mean line) are task-dependent only at 9 and 12 months of age, with higher values in rattle-shaking than free play. Since rattle-shaking elicits more stable and repetitive limb movements than the free exploration of multiple objects, we interpret our data as reflecting an increase in infants’ motor control that allows for stable body positioning and easier execution of limb movements. Infants’ motor system becomes more stable and flexible with age, allowing for flexible adaptation of behaviors to task demands.

Using Wearable Sensor Technology to Measure Motion Complexity in Infants at High Familial Risk for Autism Spectrum Disorder

BACKGROUND

Motor dysfunction has been reported as one of the first signs of atypical development in infants at high familial risk for autism spectrum disorder (ASD) (HR infants). However, studies have shown inconsistent results regarding the nature of motor dysfunction and whether it can be predictive of later ASD diagnosis. This is likely because current standardized motor assessments may not identify subtle and specific motor impairments that precede clinically observable motor dysfunction. Quantitative measures of motor development may address these limitations by providing objective evaluation of subtle motor differences in infancy.

METHODS

We used Opal wearable sensors to longitudinally evaluate full day motor activity in HR infants, and develop a measure of motion complexity. We focus on complexity of motion because optimal motion complexity is crucial to normal motor development and less complex behaviors might represent repetitive motor behaviors, a core diagnostic symptom of ASD. As proof of concept, the relationship of the motion complexity measure to developmental outcomes was examined in a small set of HR infants.

RESULTS

HR infants with a later diagnosis of ASD show lower motion complexity compared to those that do not. There is a stronger correlation between motion complexity and ASD outcome compared to outcomes of cognitive ability and adaptive skills.

CONCLUSIONS

Objective measures of motor development are needed to identify characteristics of atypical infant motor function that are sensitive and specific markers of later ASD risk. Motion complexity could be used to track early infant motor development and to discriminate HR infants that go on to develop ASD.

Leg Movement Rate Pre- and Post-Kicking Intervention in Infants with Down Syndrome

AIM

Children with Down syndrome (DS) have delayed development and atypical movements including kicking. We hypothesized that a kicking intervention would significantly increase leg movement rate.

METHODS

Nine infants, 3-5 months old, with DS used a commercially available toy that encouraged kicking. The intervention was administered in their home for 20 minutes, 5 days a week, for 8 weeks. Leg movement rate was measured using Opal wearable sensors before and after the intervention. At post-test, a secondary analysis compared infants with DS to infants with typical development (TD).

RESULTS

Average leg movement rate increased significantly from pre to post intervention, from 2253 to 2645 movements per hour of awake time (p = 0.049). Compared to data from nine infants with TD, infants with DS had a significantly lower movement rate post intervention (p = 0.002).

CONCLUSION

The infants with DS demonstrated a higher leg movement rate following an in-home kicking intervention.

3D Motion Capture May Detect Spatiotemporal Changes in Pre-Reaching Upper Extremity Movements with and without a Real-Time Constraint Condition in Infants with Perinatal Stroke and Cerebral Palsy: A Longitudinal Case Series

Perinatal stroke (PS), occurring between 20 weeks of gestation and 28 days of life, is a leading cause of hemiplegic cerebral palsy (HCP). Hallmarks of HCP are motor and sensory impairments on one side of the body—especially the arm and hand contralateral to the stroke (involved side). HCP is diagnosed months or years after the original brain injury. One effective early intervention for this population is constraint-induced movement therapy (CIMT), where the uninvolved arm is constrained by a mitt or cast, and therapeutic activities are performed with the involved arm. In this preliminary investigation, we used 3D motion capture to measure the spatiotemporal characteristics of pre-reaching upper extremity movements and any changes that occurred when constraint was applied in a real-time laboratory simulation. Participants were N = 14 full-term infants: N = six infants with typical development; and N = eight infants with PS (N = three infants with PS were later diagnosed with cerebral palsy (CP)) followed longitudinally from 2 to 6 months of age. We aimed to evaluate the feasibility of using 3D motion capture to identify the differences in the spatiotemporal characteristics of the pre-reaching upper extremity movements between the diagnosis group, involved versus uninvolved side, and with versus and without constraint applied in real time. This would be an excellent application of wearable sensors, allowing some of these measurements to be taken in a clinical or home setting.

How Many Days are Necessary to Represent Typical Daily Leg Movement Behavior for Infants at Risk of Developmental Disabilities?

BACKGROUND

Movement characteristics can differentiate between infants at risk and infants with typical development. However, it is unknown how many days are needed to accurately represent typical daily behavior for infants at risk of developmental disabilities when using wearable sensors. To consider the balance between participant burden and the amount of data collected and optimizing the efficiency of data collection, our study determined (1) how many days were necessary to represent typical movement behavior for infants at risk of developmental disabilities and (2) whether movement behavior was different on weekend days and weekdays.

METHODS

We used Opal wearable sensors to collect at least 5 days of 11 infants' leg movement data. The standard (average of 5 days) was compared with four methods (average of the first 1/2/3/4 days) using the Bland-Altman plots and the Spearman correlation coefficient. We also compared the data from the average of 2 weekend days to the average of the first 2 weekdays for 8 infants.

RESULTS

The Spearman correlation coefficient comparing the average of the first 2 days of data and the standards were all above 0.7. The absolute differences between them were all below 10% of the standards. The Bland-Altman plots showed more than 90% of the data points comparing the average of 2 days and the standards fell into the limit of agreement for each variable. The absolute difference between weekend days and weekdays for the leg movement rate, duration, average acceleration, and peak acceleration was 15.2%, 1.7%, 6.8% and 6.3% of the corresponding standard, respectively.

CONCLUSION

Our results suggest 2 days is the optimal amount of data to represent typical daily leg movement behavior of infants at risk of developmental disabilities while minimizing participant burden. Further, leg movement behavior did not differ distinctly across weekend days and weekdays. These results provide supportive evidence for an efficient amount of data collections when using wearable sensors to evaluate movement behavior in infants at risk of developmental disabilities.

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

As improvements in medicine lower infant mortality rates, more infants with neuromotor challenges survive past birth. The motor, social, and cognitive development of these infants are closely interrelated, and challenges in any of these areas can lead to developmental differences. Thus, analyzing one of these domains - the motion of young infants - can yield insights on developmental progress to help identify individuals who would benefit most from early interventions. In the presented data collection, we gathered day-long inertial motion recordings from N = 12 typically developing (TD) infants and N = 24 infants who were classified as at risk for developmental delays (AR) due to complications at or before birth. As a first research step, we used simple machine learning methods (decision trees, k-nearest neighbors, and support vector machines) to classify infants as TD or AR based on their movement recordings and demographic data. Our next aim was to predict future outcomes for the AR infants using the same simple classifiers trained from the same movement recordings and demographic data. We achieved a 94.4% overall accuracy in classifying infants as TD or AR, and an 89.5% overall accuracy predicting future outcomes for the AR infants. The addition of inertial data was much more important to producing accurate future predictions than identification of current status. This work is an important step toward helping stakeholders to monitor the developmental progress of AR infants and identify infants who may be at the greatest risk for ongoing developmental challenges.

Infant Leg Activity Intensity Before and After Naps

Wearable sensors are being used to measure intensity of infant physical activity across full days. The variability of infant activity intensity within and across days is important to study given the potential impact of physical activity on developmental trajectories. Using retrospective data, we analyzed the intensity of leg movements in 10 typically-developing infants before and after naptimes. Leg movement data were captured from 20 minutes before and after multiple events of naps across seven days for each infant. We hypothesized that leg movement intensity would be lower before a nap than after a nap potentially due to lower arousal and increased fatigue prior to attaining sleep. However, our results showed that leg movement intensity was not significantly different when comparing the 20-minute period pre- and post-naps (F(1,7)=3.91, p=0.089, ηp 2=0.358). Our results are a first step in describing patterns of infant activity across days and highlights the need for further research regarding infant energy expenditure and physical activity.

Using accelerometry for measurement of motor behavior in children: Relationship of real-world movement to standardized evaluation

BACKGROUND

When detected, children with asymmetrical motor impairment are referred for therapeutic interventions to maximize the child's ability to reach their health and developmental potential. Referal is dependent on standardized evaluation, which rarely examines upper extremity (UE) function within the context of real-world activity. Accelerometry provides an efficient method to objectively measure movement in children. The purpose of this study was to compare accelerometry to clinical assessment, specifically the Melbourne Assessment of Unilateral Upper Limb Function-2 (MA-2).

METHODS

A total of 52 children between 1-17 years of age with asymmetrical motor deficits and age matched controls participated in this study. Participants wore bilateral accelerometers for 4 x 25 h. The use ratio (UR) and mono-arm use index (MAUI) were calculated to quantify asymmetrical impairment. The Melbourne Assessment of Unilateral Upper Limb Function-2 (MA-2) was administered and compared to accelerometry variables.

RESULTS

The UR and MAUI were significantly different in children with and without deficits. The MAUI was significantly correlated with all domains of the MA-2: accuracy (r = 0.44, p = 0.026); fluency (r = 0.52, p = 0.006); dexterity (r = 0.53, p = 0.005); and range of motion (r = 0.49, p = 0.011).

CONCLUSIONS

Our findings suggest a relationship between real-world movement and clinical evaluation.

Physical activity in non-ambulatory toddlers with cerebral palsy

BACKGROUND

Children with cerebral palsy are less likely to be physically active than their peers, however there is limited evidence regarding self-initiated physical activity in toddlers who are not able, or who may never be able, to walk.

AIMS

The aim of this study was to measure self-initiated physical activity and its relationship to gross motor function and participation in non-ambulatory toddlers with cerebral palsy.

METHODS AND PROCEDURES

Participants were between the ages of 1-3 years. Physical activity during independent floor-play at home was recorded using a wearable tri-axial accelerometer worn on the child's thigh. The Gross Motor Function Measure-66 and the Child Engagement in Daily Life, a parent-reported questionnaire of participation, were administered.

OUTCOMES AND RESULTS

Data were analyzed from the twenty participants who recorded at least 90 min of floor-play (mean: 229 min), resulting in 4598 total floor-play minutes. The relationship between physical activity and gross motor function was not statistically significant (r = 0.20; p = 0.39), nor were the relationships between physical activity and participation (r = 0.05-0.09; p = 0.71-0.84).

CONCLUSIONS AND IMPLICATIONS

The results suggest physical activity during floor-play is not related to gross motor function or participation in non-ambulatory toddlers with cerebral palsy. Clinicians and researchers should independently measure physical activity, gross motor function, and participation.

Quantifying Caregiver Movement when Measuring Infant Movement across a Full Day: A Case Report

There is interest in using wearable sensors to measure infant movement patterns and physical activity, however, this approach is confounded by caregiver motion. The purpose of this study is to estimate the extent that caregiver motion confounds wearable sensor data in full-day studies of infant leg movements. We used wearable sensors to measure leg movements of a four-month-old infant across 8.5 hours, during which the infant was handled by the caregiver in a typical manner. A researcher mimicked the actions of the caregiver with a doll. We calculated 7744 left and 7107 right leg movements for the infant and 1013 left and 1115 right "leg movements" for the doll. In this case, approximately 15% of infant leg movements can be attributed to background motion of the caregiver. This case report is the first step toward removing caregiver-produced background motion from the infant wearable sensor signal. We have estimated the size of the effect and described the activities that were related to noise in the signal. Future research can characterize the noise in detail and systematically explore different methods to remove it.

Wearables for Pediatric Rehabilitation: How to Optimally Design and Use Products to Meet the Needs of Users

This article will define "wearables" as objects that interface and move with users, spanning clothing through smart devices. A novel design approach merging information from across disciplines and considering users' broad needs will be presented as the optimal approach for designing wearables that maximize usage. Three categories of wearables applicable to rehabilitation and habilitation will be explored: (1) inclusive clothing (eg, altered fit, fasteners); (2) supportive wearables (eg, orthotics, exoskeletons); and (3) smart wearables (eg, with sensors for tracking activity or controlling external devices). For each category, we will provide examples of existing and emerging wearables and potential applications for assessment and intervention with a focus on pediatric populations. We will discuss how these wearables might change task requirements and assist users for immediate effects and how they might be used with intervention activities to change users' abilities across time. It is important for rehabilitation clinicians and researchers to be engaged with the design and use of wearables so they can advocate and create better wearables for their clients and determine how to most effectively use wearables to enhance their assessment, intervention, and research practices.

How Many Days Are Necessary to Represent an Infant's Typical Daily Leg Movement Behavior Using Wearable Sensors?

BACKGROUND

Characteristics of movement can differentiate infants with typical development and infants with or at risk of developmental disabilities. We used wearable sensors to measure infants' typical movement patterns in the natural environment.

OBJECTIVE

Our objectives were to determine (1) how many days were sufficient to represent an infant's typical daily performance, and (2) if there was a difference in performance between weekdays and weekend days.

DESIGN

This was a prospective, observational study.

METHODS

We used wearable sensors to collect 7 consecutive days of data for leg movement activity, from 10 infants with typical development (1-5 months old). We identified each leg movement, and its average acceleration, peak acceleration, and duration. Bland-Altman plots were used to compare the standard (average of 7 days) with 6 options (1 day, the average of days 1 and 2, through the average of days 1 through 6). Additionally, the average of the first 2 weekdays was compared with the average of 2 weekend days.

RESULTS

The absolute difference between the average of the first 2 days and the standards fell below 10% of the standards (movement rate = 8.5%; duration = 3.7%; average acceleration = 2.8%; peak acceleration = 3.8%, respectively). The mean absolute difference between weekdays and weekends for leg movement rate, duration, average acceleration, and peak acceleration was 11.6%, 3.7%, 7.2%, and 7.3% of the corresponding standard.

LIMITATIONS

The small sample size and age range limit extrapolation of the results.

CONCLUSIONS

Our results suggest the best option is to collect data for 2 consecutive days and that movement did not differ between weekdays and weekend days. Our results will inform the clinical measurement of full-day infant leg movement for neuromotor assessment and outcome purposes.

Sensor Measures of Symmetry Quantify Upper Limb Movement in the Natural Environment Across the Lifespan

Knowledge of upper limb activity in the natural environment is critical for evaluating the effectiveness of rehabilitation services. Wearable sensors allow efficient collection of these data and have the potential to be less burdensome than self-report measures of activity. Sensors can capture many different variables of activity and daily performance, many of which could be useful in identifying deviation from typical movement behavior or measuring outcomes from rehabilitation interventions. Although it has potential, sensor measurement is just emerging, and there is a lack of consensus regarding which variables of daily performance are valid, sensitive, specific, and useful. We propose that symmetry of full-day upper limb movement is a key variable. We describe here that symmetry is valid, robustly observed within a narrow range across the lifespan in typical development, and shows evidence of being different in populations with neuromotor impairment. Key next steps include the determination of sensitivity, specificity, minimal detectable change, and minimal clinically important change/difference. This information is needed to determine whether an individual belongs to the typical or atypical group, whether change has occurred, and whether that change is beneficial.

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.