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

Abstract

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.

The dynamics of dyadic interactions between people of different ethnicities depend on their identification with all humanity

“…This is in line with studies showing that higher complexity is related to lower RQA parameters (e.g. 22,23 ). A dyadic interaction lacking structure (i.e., lower complexity) will be less efficient since both interactive partners will not adjust their movements to one another as opposed to a structured and efficient interaction (i.e., high complexity).…”

Evaluating the Effectiveness of Complexity Features of Eye Movement on Computer Activities Detection

“…The basic nature of complexity-based features that are containing “implicit” information about body responses to the human activity and states [ 46 , 47 ] has benefited AI models to help them to overcome the prediction confusedness (compare Table 4 with Table 5 ). As was expected, the findings in this study prove that complexity analysis is also suitable for eye-movement-based data, as useful as its usage in human heart rate [ 16 ], cerebral hemodynamics [ 17 ], blood pressure [ 18 ], and body movements [ 19 , 48 ] data. Moreover, the experimental procedure that resulted in moderate head movement also confirmed that AI models would be suitable for everyday use in distinguishing computer activities in daily life.…”

“…Meanwhile, complexity analysis [ 11 ] was recently used in certain human biometric data comprising heart rate [ 16 ], cerebral hemodynamics [ 17 ], blood pressure [ 18 ], and infants’ limb movements [ 19 ]. The use of the complexity of these biological data can describe the human states related to their health conditions [ 16 , 17 , 18 ] and activities [ 19 , 20 ]. The benefit is the potential application to eye-movement features.…”

Modeling gross motor developmental curves of extremely and very preterm infants using the AIMS home-video method

“…Lastly, with new and advanced technologies [66][67][68], it is perhaps possible to assess infants' development more frequently. This will give us even more detailed information about infant motor development and perhaps an indication of periods when motor development is subject to change.…”