Design and validation of a smart garment to measure positioning practices of parents with young infants

Physical Activity in Pre-Ambulatory Children with Cerebral Palsy: An Exploratory Validation Study to Distinguish Active vs. Sedentary Time Using Wearable Sensors

Wearable inertial sensor technology affords opportunities to record the physical activity of young children in their natural environments. The interpretation of these data, however, requires validation. The purpose of this study was to develop and establish the criterion validity of a method of quantifying active and sedentary physical activity using an inertial sensor for pre-ambulatory children with cerebral palsy. Ten participants were video recorded during 30 min physical therapy sessions that encouraged gross motor play activities, and the video recording was behaviorally coded to identify active and sedentary time. A receiver operating characteristic curve identified the optimal threshold to maximize true positive and minimize false positive active time for eight participants in the development dataset. The threshold was 0.417 m/s2 and was then validated with the remaining two participants; the percent of true positives and true negatives was 92.2 and 89.7%, respectively. We conclude that there is potential for raw sensor data to be used to quantify active and sedentary time in pre-ambulatory children with physical disability, and raw acceleration data may be more generalizable than the sensor-specific activity counts commonly reported in the literature.

Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter?

The efficient classification of body position is crucial for monitoring infants' motor development. It may fast-track the early detection of developmental issues related not only to the acquisition of motor milestones but also to postural stability and movement patterns. In turn, this may facilitate and enhance opportunities for early intervention that are crucial for promoting healthy growth and development. The manual classification of human body position based on video recordings is labour-intensive, leading to the adoption of Inertial Motion Unit (IMU) sensors. IMUs measure acceleration, angular velocity, and magnetic field intensity, enabling the automated classification of body position. Many research teams are currently employing supervised machine learning classifiers that utilise hand-crafted features for data segment classification. In this study, we used a longitudinal dataset of IMU recordings made in the lab in three different play activities of infants aged 4-12 months. The classification was conducted based on manually annotated video recordings. We found superior performance of the CatBoost Classifier over the Random Forest Classifier in the task of classifying five positions based on IMU sensor data from infants, yielding excellent classification accuracy of the Supine (97.7%), Sitting (93.5%), and Prone (89.9%) positions. Moreover, using data ablation experiments and analysing the SHAP (SHapley Additive exPlanations) values, the study assessed the importance of various groups of features from both the time and frequency domains. The results highlight that both accelerometer and magnetometer data, especially their statistical characteristics, are critical contributors to improving the accuracy of body position classification.

Long-form recording of infant body position in the home using wearable inertial sensors

Long-form audio recordings have had a transformational effect on the study of infant language acquisition by using mobile, unobtrusive devices to gather full-day, real-time data that can be automatically scored. How can we produce similar data in service of measuring infants' everyday motor behaviors, such as body position? The aim of the current study was to validate long-form recordings of infant position (supine, prone, sitting, upright, held by caregiver) based on machine learning classification of data from inertial sensors worn on infants' ankles and thighs. Using over 100 h of video recordings synchronized with inertial sensor data from infants in their homes, we demonstrate that body position classifications are sufficiently accurate to measure infant behavior. Moreover, classification remained accurate when predicting behavior later in the session when infants and caregivers were unsupervised and went about their normal activities, showing that the method can handle the challenge of measuring unconstrained, natural activity. Next, we show that the inertial sensing method has convergent validity by replicating age differences in body position found using other methods with full-day data captured from inertial sensors. We end the paper with a discussion of the novel opportunities that long-form motor recordings afford for understanding infant learning and development.

Accelerometer Thresholds for Estimating Physical Activity Intensity Levels in Infants: A Preliminary Study

Lack of physical activity (PA) at a young age can result in health issues. Thus, monitoring PA is important. Wearable accelerometers are the preferred tool to monitor PA in children. Validated thresholds are used to classify activity intensity levels, e.g., sedentary, light, and moderate-to-vigorous, in ambulatory children. No previous work has developed accelerometer thresholds for infancy (pre-ambulatory children). Therefore, this work aims to develop accelerometer thresholds for PA intensity levels in pre-ambulatory infants. Infants (n = 10) were placed in a supine position and allowed free movement. Their movements were synchronously captured using video cameras and accelerometers worn on each ankle. The video data were labeled by activity intensity level (sedentary, light, and moderate-to-vigorous) in two-second epochs using observational rating (gold standard). Accelerometer thresholds were developed for acceleration and jerk using two optimization approaches. Four sets of thresholds were developed for dual (two ankles) and for single-worn (one ankle) accelerometers. Of these, for a typical use case, we recommend using acceleration-based thresholds of 1.00 m/s to distinguish sedentary and light activity and 2.60 m/s to distinguish light and moderate-to-vigorous activity. Acceleration and jerk are both suitable for measuring PA.

Sitting Capacity and Performance in Infants with Typical Development and Infants with Motor Delay

AIMS

Infants with neuromotor disorders demonstrate delays in sitting skills (decreased capacity) and are less likely to maintain independent sitting during play than their peers with typical development (decreased performance). This study aimed to quantify developmental trajectories of sitting capacity and sitting performance in infants with typical development and infants with significant motor delay and to assess whether the relationship between capacity and performance differs between the groups.

METHODS

Typically developing infants (n = 35) and infants with significant motor delay (n = 31) were assessed longitudinally over a year following early sitting readiness. The Gross Motor Function Measure (GMFM) Sitting Dimension was used to assess sitting capacity, and a 5-min free play observation was used to assess sitting performance.

RESULTS

Both capacity and performance increased at a faster rate initially, with more deceleration across time, in infants with typical development compared to infants with motor delay. At lower GMFM scores, changes in GMFM sitting were associated with larger changes in independent sitting for infants with typical development, and the association between GMFM sitting and independent sitting varied more across GMFM scores for typically developing infants.

CONCLUSIONS

Intervention and assessment for infants with motor delay should target both sitting capacity and sitting performance.

The outcomes of a healing environment and clustering nursing care on premature infants' vital signs, pain, and sleeping

Our study aimed to assess the effects of creating a healing environment and clustering nursing care on premature infants' vital signs, pain, and sleeping. The study had an experimental research design for the control and study group, each with 53 premature infants. We collected the data through the Vital Signs Sheet, Premature Infant Pain Profile, and Neonatal behavioral state. We used T-tests and chi-square tests to assess the differences between groups. There was a highly statistically significant difference between the study and control groups concerning respiration (p-value<0.01) and heart rate, systolic blood pressure, and O² saturation (p-value<0.05). 90.6% of participants in the study group had a mild total premature infant pain profile, while 37.7% of the control group had a moderate total premature infant pain profile score. Applying a healing environment and clustering nursing care significantly improved respiration, heart rate, oxygen saturation, and systolic blood pressure. Furthermore, it increased sleep time and decreased wake state and pain score.

Systematic review of accelerometer-based methods for 24-h physical behavior assessment in young children (0-5 years old)

BACKGROUND

Accurate accelerometer-based methods are required for assessment of 24-h physical behavior in young children. We aimed to summarize evidence on measurement properties of accelerometer-based methods for assessing 24-h physical behavior in young children.

METHODS

We searched PubMed (MEDLINE) up to June 2021 for studies evaluating reliability or validity of accelerometer-based methods for assessing physical activity (PA), sedentary behavior (SB), or sleep in 0-5-year-olds. Studies using a subjective comparison measure or an accelerometer-based device that did not directly output time series data were excluded. We developed a Checklist for Assessing the Methodological Quality of studies using Accelerometer-based Methods (CAMQAM) inspired by COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN).

RESULTS

Sixty-two studies were included, examining conventional cut-point-based methods or multi-parameter methods. For infants (0-12 months), several multi-parameter methods proved valid for classifying SB and PA. From three months of age, methods were valid for identifying sleep. In toddlers (1-3 years), cut-points appeared valid for distinguishing SB and light PA (LPA) from moderate-to-vigorous PA (MVPA). One multi-parameter method distinguished toddler specific SB. For sleep, no studies were found in toddlers. In preschoolers (3-5 years), valid hip and wrist cut-points for assessing SB, LPA, MVPA, and wrist cut-points for sleep were identified. Several multi-parameter methods proved valid for identifying SB, LPA, and MVPA, and sleep. Despite promising results of multi-parameter methods, few models were open-source. While most studies used a single device or axis to measure physical behavior, more promising results were found when combining data derived from different sensor placements or multiple axes.

CONCLUSIONS

Up to age three, valid cut-points to assess 24-h physical behavior were lacking, while multi-parameter methods proved valid for distinguishing some waking behaviors. For preschoolers, valid cut-points and algorithms were identified for all physical behaviors. Overall, we recommend more high-quality studies evaluating 24-h accelerometer data from multiple sensor placements and axes for physical behavior assessment. Standardized protocols focusing on including well-defined physical behaviors in different settings representative for children's developmental stage are required. Using our CAMQAM checklist may further improve methodological study quality.

PROSPERO REGISTRATION NUMBER

CRD42020184751.

Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants

Background

Early neurodevelopmental care needs better, effective and objective solutions for assessing infants' motor abilities. Novel wearable technology opens possibilities for characterizing spontaneous movement behavior. This work seeks to construct and validate a generalizable, scalable, and effective method to measure infants' spontaneous motor abilities across all motor milestones from lying supine to fluent walking.

Methods

A multi-sensor infant wearable was constructed, and 59 infants (age 5-19 months) were recorded during their spontaneous play. A novel gross motor description scheme was used for human visual classification of postures and movements at a second-level time resolution. A deep learning -based classifier was then trained to mimic human annotations, and aggregated recording-level outputs were used to provide posture- and movement-specific developmental trajectories, which enabled more holistic assessments of motor maturity.

Results

Recordings were technically successful in all infants, and the algorithmic analysis showed human-equivalent-level accuracy in quantifying the observed postures and movements. The aggregated recordings were used to train an algorithm for predicting a novel neurodevelopmental measure, Baba Infant Motor Score (BIMS). This index estimates maturity of infants' motor abilities, and it correlates very strongly (Pearson's r = 0.89, p < 1e-20) to the chronological age of the infant.

Conclusions

The results show that out-of-hospital assessment of infants' motor ability is possible using a multi-sensor wearable. The algorithmic analysis provides metrics of motility that are transparent, objective, intuitively interpretable, and they link strongly to infants' age. Such a solution could be automated and scaled to a global extent, holding promise for functional benchmarking in individualized patient care or early intervention trials.

A model for using developmental science to create effective early intervention programs and technologies to improve children's developmental outcomes

Children born with a variety of environmental or medical risk factors may exhibit delays in global development. Very often, such delays are identified at preschool or school age, when children are severely overdue for effective early interventions that can alleviate the delays. This chapter proposes a conceptual model of child development to inform the creation of interventions and rehabilitative technologies that can be provided very early in development, throughout the first year of life, to optimize children's future developmental outcomes. The model suggests that early sensorimotor skills are antecedent and foundational for future motor, cognitive, language, and social development. As an example, this chapter describes how children's early postural control and exploratory movements facilitate the development of future object exploration behaviors that provide enhanced opportunities for learning and advance children's motor, cognitive, language, and social development. An understanding of the developmental pathways in the model can enable the design of effective intervention programs and rehabilitative technologies that target sensorimotor skills in the first year of life with the goal of minimizing or ameliorating the delays that are typically identified at preschool or school age. Specific examples of early interventions and rehabilitative technologies that have effectively advanced children's motor and cognitive development by targeting early sensorimotor skills and behaviors are provided.

A Contactless Method for Measuring Full-Day, Naturalistic Motor Behavior Using Wearable Inertial Sensors

How can researchers best measure infants' motor experiences in the home? Body position-whether infants are held, supine, prone, sitting, or upright-is an important developmental experience. However, the standard way of measuring infant body position, video recording by an experimenter in the home, can only capture short instances, may bias measurements, and conflicts with physical distancing guidelines resulting from the COVID-19 pandemic. Here, we introduce and validate an alternative method that uses machine learning algorithms to classify infants' body position from a set of wearable inertial sensors. A laboratory study of 15 infants demonstrated that the method was sufficiently accurate to measure individual differences in the time that infants spent in each body position. Two case studies showed the feasibility of applying this method to testing infants in the home using a contactless equipment drop-off procedure.