A new innovation makes it possible, for the first time, to quantitatively assess children’s spontaneous movement in the natural environment.
Researchers have developed a smart jumpsuit, or a garment that accurately measures the spontaneous and voluntary movement of infants from the age of five months.
Details on their motility help in assessing abnormal neurological development, among other things.
The study on the smart jumpsuit and the related analysis method applied to 7-month-old infants was published in the Scientific Reports journal.
In the future, the jumpsuit can also be used to study older children.
The assessment of spontaneous and voluntary movements is part of the neurological examination of infants.
Previously, the quantitative tracking of children’s spontaneous motility in the natural environment has not been possible. Instead, children have been primarily qualitatively assessed at the physician’s or physiotherapist’s practice, which requires taking into account the fact that the infant’s behaviour in the practice setting does not necessarily entirely match that seen at home.
“The smart jumpsuit provides us with the first opportunity to quantify infants’ spontaneous and voluntary movements outside the laboratory.
The child can be sent back home with the suit for the rest of the day. The next day, it will be returned to the hospital where the results will then be processed,” explains Sampsa Vanhatalo, professor of clinical neurophysiology at the University of Helsinki.
Vanhatalo says that the new analysis method quantifies infant motility as reliably as a human being would be able to do by viewing a video recording.
After the measurement, the infant’s actual movements and physical positions will be known to the second, after which computational measures can be applied to the data.
“This is a revolutionary step forward. The measurements provide a tool to detect the precise variation in motility from the age of five months, something which medical smart clothes have not been able to do until now.”
Neurological abnormalities should be detected early on
The data gleaned by the smart jumpsuit is valuable, since the detection of abnormalities in the neurological development of infants at an early stage enables early support.
Brain plasticity is at its strongest in early childhood, and is benefited by measures supporting development, which are targeted at recurring everyday activities.
At least 5% of Finnish children suffer from problems associated with language development, attention regulation and motor development.
Often, such problems overlap. The pathogenic mechanisms underlying developmental disorders are complex, but preterm birth, perinatal brain damage and the lack of early care, as well as insufficient stimulation in the growth environment aggravate the risk of developmental problems.
According to Leena Haataja, professor of paediatric neurology, developmental disorders in today’s pressure-dominated world pose a considerable risk that can lead to learning difficulties and obstacles in the competition for education and jobs. Furthermore, they are a risk factor associated with exclusion from contemporary society.
The smart jumpsuit provides the first opportunity to quantify infants’ spontaneous movements outside the laboratory.
“The early identification of developmental disorders and support for infants’ everyday functional capacity in interaction with the family and the growth environment constitute a significant factor on the level of individuals, families and society,” Haataja notes.
In the future, the smart jumpsuit can be used for the objective measurement of how various therapies and treatments affect children’s development.
“This is the million-dollar question in Western healthcare. In addition, we may be able to quantify how early motor development associates with later cognitive development,” Vanhatalo says.
The smart jumpsuit was developed under the Rhythms in Infant Brain (RIB) project, part of the Health from Science (TERVA) programme funded by the Academy of Finland, the Foundation for Pediatric Research and the Finnish Brain Foundation.
The multidisciplinary research group, which operates in the New Children’s Hospital, is headed by neurophysiologist Sampsa Vanhatalo and paediatric neurologist Leena Haataja. In addition to physicians, the group comprises psychologists, physiotherapists, nurses and engineers.
The textile and usability design of the smart jumpsuit was coordinated by researcher Elina Ilén, while researchers Manu Airaksinen and Okko Räsänen from Aalto University were in charge of AI analyses. The project utilised the Movesense sensor, an open-source device developed in Finland by Suunto, and a mobile application developed by the German company Kaasa.
A key global healthcare challenge is the early recognition of infants that eventually develop lifelong neurocognitive disabilities. More than every tenth infant is considered to be at neurodevelopmental risk1 due to their neonatal medical adversities, such as prematurity, birth asphyxia, stroke, metabolic derangements and intrauterine substance exposures.
An early therapeutic intervention would be optimal for reducing the ensuing lifelong toll to individuals and societies, although it has been challenging to efficiently target early intervention to those who actually would benefit from it2.
A thorough screening of all risk infants with extensive test batteries and/or brain imaging techniques is not plausible in most parts of the world, and it is not justifiable even in the most developed and wealthy nations.
There is hence a rising need to develop generalizable, scalable, objective, and effective solutions for early neurodevelopmental screening.
Such methods would need to be robust to random variability that may arise from all parties involved: the infant him/herself, skills of the health care professional, testing environment, as well as recording methods.
Furthermore, a wide biological variation which is inherent to typical neurodevelopment has to be considered. Indeed, requirement for adequate robustness has been a key challenge in the recent work3.
Phenomenologically, assessment of infants’ spontaneous behavior has recently gained a lot of interest for three reasons:
First, the lab or hospital environments are artificial from a child’s perspective, and hence challenging for assessing their true neurodevelopmental performance4. Second, the novel recording and analysis methodologies have made it possible to study participants’ spontaneous activities in both lab and other environments5,6.
Third, both fundamental and applied research has shown that infant’s spontaneous movements may provide an important global window to the infant brain function7,8.
Besides bodily movements per se, development of control of posture and intentional voluntary movements can be seen as parallel processes forming one perceptual-motor system which also involves higher cognitive functions9.
Characterization of infants’ typical pattern of variation in different postures and movement activity over longer time could be used as a tool for early screening of infants at neurodevelopmental risks.
Ideally, such a system would consist of an easy-to-use recording setup applicable to home environments, followed by an automated analysis pipeline for objective and quantitative assessment. Widely used observation protocols have been developed for assessment of spontaneous movements in neonatal period and early infancy2,10.
However, they are neither genuinely quantitative nor allow longitudinal tracking beyond four months of age.
One option for monitoring infant movements at their homes could consist of intelligent wearables with integrated sensors.
There has been significant recent progress in the development of intelligent wearables for sports and leisure clothing in adults11. However, we are not aware of standards or open solutions for multi-sensor-based movement analysis for infants.
Our present work aims to fill this gap by describing a relatively inexpensive comfortable-to-wear, and easy-to-use intelligent wearable, a smart jumpsuit that can be used for monitoring and quantifying key postures and movement patterns of independently moving infants.
In addition to the design of the jumpsuit itself, we have developed a new protocol to visually classify and annotate independent movements of infants yet to acquire upright posture.
We also developed a machine learning -based classifier to automatically recognize the set of postures and movements covered by the annotation scheme by using the sensory data available from the jumpsuit sensors, including a novel way to deal with inter-annotator inconsistencies inherently present in the human annotations used to train the classifier.
Performance of the resulting system was assessed against multiple human raters on data from infants previously unseen by the classifier. The results show that the system achieves posture and movement recognition accuracy comparable to human raters on the same data, indicating the feasibility of automatic assessment of spontaneous infant movements using the proposed smart jumpsuit.
This study shows that it is possible to construct a comfortable-to-wear intelligent infant wearable with a signal processing pipeline that allows quantitative tracking of independent movement activities of infants with high accuracy. We developed a novel annotation scheme to classify infant postures and movements into a number of key categories, and demonstrated how an automatic classifier can reach human-like consistency in movement and posture recognition.
In addition, we described a principled probabilistic approach to exploit the inter-rater inconsistencies in the human annotations used to train the classifier. Finally, we demonstrated that a multi-sensor setup is required for maximal movement classification performance. Our present work extends the prior work from adult studies23 that clinically relevant movement tracking and quantification is possible in infants as well.
The present work goes beyond the prior literature by constructing and demonstrating the feasibility of the first multi-sensor wearable for infants that allows non-intrusive, cheap, and technically achievable measurement of infants’ posture and independent movements.
The result is a full smart jumpsuit system that could be implemented in out-of-hospital recordings, at least in the clinical research context.
From the technical point of view, our findings show that an SVM classifier based on standard signal-level features is sufficient for posture tracking and adequate for detecting some categories of movement. However, the SVM struggles in recognizing certain key infant motor patterns, such as crawling posture and pivoting, which are crucial milestones for a normal neurological development29.
For this reason, an end-to-end CNN classifier was designed for the task, as similar CNN architectures have previously demonstrated state-of-the-art performance in adult-based human activity recognition23.
The resulting CNN classifier yielded statistically significant and much needed/required improvements in the movement tracking. Comparison to human annotator agreement levels shows that the CNN achieves classification performance comparable to human annotator consistency, and it could therefore be used as an independent automatic measure of infant motility.
Practical aspects of jumpsuit analysis
Literature on movement analysis based on IMU sensors has grown rapidly and a wide range of analytic tools have been developed to analyse movement activity at different levels30,31. A key challenge has been in search for unified recording settings and/or classification tasks32.
It now seems clear that solutions need to be tailored specifically, at least for different subject groups and tasks31. For instance, there may be substantial day-to-day variation in motility, which needs to define a balance between the added information gain vs practical costs of longer recording periods33. Preliminary studies have shown, however, that quantitative movement analysis of infants may be possible with accuracy that even allows clinical outcome predictions3,34,35.
Designing an infant medical wearable of this kind is a multidisciplinary challenge3. At the patient level, there are practical challenges such as wearing comfort to allow normal motility. Here, we chose an infant swim suit as the model for cut design; the sensor placements were such that they would be likely ignored by most infants to allow undisturbed motility.
We initially also piloted with distal sensor placements, but they were not found to bring significant benefits for automated analysis. Instead, distal sensors tended to be mechanically more unstable and easily attracted infants’ attention.
At the level of operator, the full recording system including the mobile device for data collection needs to be easy enough to use, while the collection of synchronized data must be reliable throughout the session.
A further improvement of the system could be achieved by development of higher memory capacity into the sensor modules, thereby lifting the need for continuous data streaming. An overarching issue is the need to reduce complexity of the whole setup.
Our smart jumpsuit design was markedly challenged by the chosen multi-sensor setup, which required reliable wireless collection of synchronized data at high rates over Bluetooth transmission. The choice to use multiple sensors was intuitively reasoned by a potential for better movement discrimination in follow-up research.
Comparison of classification results between different sensor configurations shows that posture detection alone may be relatively reliable from even one sensor. However additional sensors bring substantially more accuracy to recognition of movement patterns. In the context of prospected out-of-hospital studies, it is also important to consider technical reliability, including participant’s compliance and hardware-related issues. Any additional data, such as redundant sensor information, may prove invaluable in the real life settings of infants’ native environment (see also3,36).
Multisensor recording of the present kind opens many potential ways for further analyses. For instance, one can readily envision quantitative, posture context-dependent assessment, where computational analyses account for different postures.
For instance, recent literature reports computational measures of spontaneous hand or leg movements that are highly predictive of later neurodevelopment9,34.
Infants’ intentional hand or leg movements are heavily modulated by posture, yet no method is available to allow their analysis separately for different posture contexts; an approach that becomes readily available with the methodology described in out present work.
Moreover, posture and movement tracking enables context-specific analysis of heuristic features (e.g.35,37,38) such as movement symmetry and limb synchrony, the well established metrics in the analysis of adults’ spontaneous motility39.
Recent literature has underscored the need to develop functional growth charts to allow evidence-based tracking of individual neurodevelopment3,37.
On another note, recent changes in infant care practices have emphasized “tummy time”, placing infants on their stomach to play when awake, as an important posture context to support early neurodevelopment40. All these trends will benefit from an established, automated methodology that allows tracking posture and movement patterns.
As an initial proof of concept, we showed how posture and movement tracking may differentiate high and low performing infants by simply quantifying mere incidence of movement categories.
Further efforts with context-dependent quantitation are likely to boost the information value. An obvious practical use case of such method would be tracing atypical patterns in motor development. Moreover, a reliable quantitative tool for motor activity tracking holds significant promise for a functional biomarker, i.e., providing much awaited evidence for the efficacy of early therapeutic interventions2,41.
University of Helsinki