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Artificial intelligence tools used to analyze video recordings of toddlers playing with bubbles can identify subtle differences in how children on the autism spectrum move their hands. Specifically, toddlers later diagnosed with autism move their hands across a wider upward and downward distance while flapping than children without the diagnosis, according to research published in the journal Developmental Science. These tiny variations in physical movement could eventually help clinicians recognize behavioral patterns in very young children.
Repetitive hand flapping involves a child repeatedly shaking or oscillating their hands and arms over a period of time. It is a common behavior among toddlers, particularly during moments of high excitement or stimulation. In older children and adults, frequent or intense hand flapping is frequently associated with the autism spectrum.
Because both neurotypical children and children on the autism spectrum flap their hands, discerning whether the behavior is a natural part of early emotional expression or an early indicator of atypical development is challenging. The second year of life is a period of rapid developmental change. Early diagnosis often relies on overlapping behavioral patterns that can be difficult to quantify through standard observation alone.
Historically, researchers have studied these early behaviors by having clinical assistants watch hours of video recordings and manually note the onset and offset of a child’s movements. This manual annotation method tracks whether a behavior occurs and for how long. It does not effectively capture the physical intensity, speed, or precise mechanical qualities of the movement itself.
To explore whether the physical characteristics of hand flapping could help separate typical behaviors from atypical ones, an investigative team examined video footage of children engaging in structured play. Jan Stenum, a researcher in the Department of Physical Medicine and Rehabilitation at the Johns Hopkins University School of Medicine, led the investigation alongside colleagues at the Kennedy Krieger Institute in Baltimore.
The researchers analyzed clinical assessments from a larger historical study of early childhood development. They focused on a final group of 28 toddlers between 13 and 16 months of age. Half of these children continuously displayed typical developmental milestones, while the other half later received a formal autism diagnosis around the age of three.
The research team extracted a brief, three-minute segment of video for each child. During this portion of the clinical assessment, an examiner engaged the toddler in a standard bubble play activity. Bubbles routinely elicit excitement and visual attention, making the activity highly likely to trigger spontaneous hand flapping.
To evaluate the specific mechanical properties of the toddlers’ movements, the team utilized a form of artificial intelligence called computer vision. Computer vision software trains computers to interpret visual information from digital images or videos. The researchers selected an algorithm known as AlphaPose, which automatically identifies and tracks anatomical landmarks on the human body.
When applied to the digital videos, the software mapped digital coordinates onto the toddlers’ joints, including their shoulders, elbows, wrists, and fingertips. This created a moving digital stick figure that tracked the exact position of the child’s limbs pixel by pixel throughout the recording. This provided the researchers with objective data about the children’s movement characteristics.
The team calculated two primary measurements from the digital tracking data. They measured the amplitude, which is the vertical distance the hands traveled up and down during a flapping motion. They also calculated the frequency, which measures the rate or speed of the hand flaps per second.
Because the toddlers occasionally stood close to the camera lens and sometimes stood farther away, the software adjusted the measurements based on each child’s physical proportions. The team normalized the amplitude of the hand flaps relative to the height of each toddler’s torso. This step ensured that variations in body size or camera distance did not distort the recorded data.
When analyzing each instance of hand flapping individually, the researchers found a measurable group distinction. Toddlers who later received an autism diagnosis displayed a higher amplitude of movement during individual flapping events compared to the other children. Their hands moved through a much larger vertical distance, reflecting a more vigorous up-and-down physical motion.
The frequency of the flaps did not follow this pattern. The team noticed that the speed of the hand movements did not reveal a statistically significant difference between the two sets of toddlers. Both groups of children oscillated their hands at roughly the same rate during their respective bursts of excitement.
When the researchers averaged the data instead of looking at individual physical events, the differences entirely disappeared. If the team combined all of a single child’s hand flapping events into one average score for that participant, the final numbers did not show a statistically significant difference in either amplitude or frequency between the two groups.
The researchers suggested that this calculation effect occurs because toddler behavior varies widely from moment to moment. A child might flap their hands vigorously during one burst of excitement but show much smaller movements a minute later. Averaging all these instances together erases the extreme highs and lows of their physical expressions.
The findings emphasize the importance of analyzing moment-to-moment behaviors rather than relying on broad summaries. Catching these tiny, dynamic expressions required the software to evaluate each brief period of movement on its own merits rather than blending them into a generalized behavioral profile.
The research team noted several limitations in their investigation, starting with the small study sample of just 28 children. Evaluating larger groups of toddlers will be necessary to confirm if these elevated movement patterns consistently appear in broader populations.
The study also narrowly focused on a single behavior measured during a highly specific three-minute window of play. Toddlers experience a wide range of emotions and settings over a given day. Longer recording times spanning various environments could provide a more complete picture of how their physical movements fluctuate.
Additionally, half of the children in the comparison group had older siblings with an autism diagnosis. Siblings of autistic children have roughly a twenty percent likelihood of eventually receiving a diagnosis themselves. Even those who do not meet the full diagnostic criteria often display early but subclinical traits related to autism, which could blur the statistical boundaries between the two groups.
Camera hardware also presents a challenge and an opportunity for future tracking efforts. The videos used in the study were collected more than twenty years ago using standard equipment of the era. Modern smartphone cameras capture video at much higher resolutions, meaning future software applications could track these joint coordinates with far greater precision.
While automated video tracking shows promise in capturing nuanced aspects of toddler movement, the researchers cautioned that the technology cannot replace clinical expertise. Direct observation, thorough developmental histories, and face-to-face cognitive testing remain standard practice. Instead, computational tracking could eventually offer an extra, objective tool to assist clinicians during early childhood evaluations.
The study, “Quantifying Repetitive Hand Flapping Kinematics in Autistic and Non-Autistic Toddlers Using Video-Based Pose Estimation,” was authored by Jan Stenum, Elizabeth Eiler, Ryan T. Roemmich, Rebecca Landa, and Rachel Reetzke.
