Real-Time Assembly of Coordination Patterns in Human Infants

Ossmy, Ori; Adolph, Karen E

doi:10.1016/j.cub.2020.08.073

Cited by 22 publications

(21 citation statements)

References 45 publications

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“…Hence, its presence in the first year of life is in line with the idea that complex functions do not need to be innate, as they can rather emerge from the early accumulation of evidence 5,34 , combined with powerful learning mechanisms 3 and fundamental attentional biases 35 . Recent theoretical and empirical work favours the idea that high-level cognitive systems can be generated via sub-symbolic dynamics 16,36 .…”

Section: Discussionsupporting

confidence: 62%

Eight-month-old infants meta-learn by downweighting irrelevant evidence

Poli¹,

Ghilardi²,

Mars³

et al. 2021

Preprint

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Infants learn to navigate the complexity of the physical and social world at an outstanding pace, but how they accomplish this learning is still unknown. Recent advances in human and artificial intelligence research propose that a key feature to achieve quick and efficient learning is meta-learning, the ability to make use of prior experiences to optimize how future information is acquired. Here we show that 8-month-old infants successfully engage in meta-learning within very short timespans. We developed a Bayesian model that captures how infants attribute informativity to incoming events, and how this process is optimized by the meta-parameters of their hierarchical models over the task structure. We fitted the model using infants’ gaze behaviour during a learning task. Our results reveal that infants do not simply accumulate experiences, but actively use them to generate new inductive biases that allow learning to proceed faster in the future.

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Section: Discussionsupporting

confidence: 62%

Eight-month-old infants meta-learn by downweighting irrelevant evidence

Poli¹,

Ghilardi²,

Mars³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition to this online processing, we also conducted an offline reestimation of participants' bones for the recorded videos as accumulated 2D images because the online process was based on a best-effort format (i.e., changeable). This was motivated by a previous study that revealed toddlers' motor behaviors in similar offline motion analysis with their natural dynamics without wearing any devices 17 . As a result, we were able to show evidence of delicate changes in their reaching strategies where a preprocess of the children's noisy data was inevitable at first.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, we used Openpose to extend the targeted body parts from the face to the whole body. Although Openpose detects bones from two-dimensional RGB images, it has been reported that its detection is reliable even for infants with much smaller body sizes 17 . As a result, we implemented augmented reality (AR) to display virtual marks in realtime on various body parts.…”

Section: Introductionmentioning

confidence: 99%

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Tracking Action in XR Mark Test Reveals Developing Body Representation Among Toddlers

Miyazaki

Asai

Ban³

et al. 2021

Preprint

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The mark test is a popular test for self-recognition. Although the extent to which self-recognition can be assessed remains controversial, the test elicits visually guided, self-oriented, and spontaneous reaching movements. In this study, we demonstrated that this self-oriented reaching is suitable for estimating sensorimotor body representation in toddlers. We developed a non-verbal task (Bodytoypo) to assess the localization of body parts by gamifying the mark test and conducted it with thirty 2- and 3-year old children. Specifically, we detected the children's skeletal data in real-time, displayed virtual marks on various parts of their body, and estimated their reaction time and accuracy of body part localization. Subsequently, a statistical-based automated analysis using 2-D image processing and conventional frame-by-frame coding were performed. The results revealed developmental changes in the children's reaching strategies. A few errors were observed around the face. A reduction in the error rate for joint and movable areas was observed in children between the ages of 2 and 3 years. An analysis of movement trajectories using a combination of image processing and machine learning algorithms showed that 2-year-olds acquired visually guided reaching (feedback control) from ballistic exploratory reaching and 3-year-olds acquired rapid and predictive reaching (feedforward control) from visually guided cautious reaching. It was also found that the accuracy of localization could be predicted by examining the coordination of body parts. Evaluation of the developmental changes in self-oriented reaching reveals new possibilities for the mark test and development of body representation.

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“…For example, pose estimation has been used to study human locomotion [15,34,35] and provide kinematic measures such as lower limb joint angles; spatiotemporal measures such as gait speed, step length, and step time; and clinical ratings such as the Gait Deviation Index in patients with cerebral palsy or MDS-UPDRS gait scores for persons with Parkinson's disease. Other studies have used pose estimation to assess neuromotor risk and development in human infants [36,37]. These areas of application are introduced briefly here, but will be covered in greater detail in later sections of this manuscript.…”

Section: What Is Pose Estimation?mentioning

confidence: 99%

Applications of Pose Estimation in Human Health and Performance across the Lifespan

Stenum

Cherry‐Allen

Pyles

et al. 2021

Sensors

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The emergence of pose estimation algorithms represents a potential paradigm shift in the study and assessment of human movement. Human pose estimation algorithms leverage advances in computer vision to track human movement automatically from simple videos recorded using common household devices with relatively low-cost cameras (e.g., smartphones, tablets, laptop computers). In our view, these technologies offer clear and exciting potential to make measurement of human movement substantially more accessible; for example, a clinician could perform a quantitative motor assessment directly in a patient’s home, a researcher without access to expensive motion capture equipment could analyze movement kinematics using a smartphone video, and a coach could evaluate player performance with video recordings directly from the field. In this review, we combine expertise and perspectives from physical therapy, speech-language pathology, movement science, and engineering to provide insight into applications of pose estimation in human health and performance. We focus specifically on applications in areas of human development, performance optimization, injury prevention, and motor assessment of persons with neurologic damage or disease. We review relevant literature, share interdisciplinary viewpoints on future applications of these technologies to improve human health and performance, and discuss perceived limitations.

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Real-Time Assembly of Coordination Patterns in Human Infants

Cited by 22 publications

References 45 publications

Eight-month-old infants meta-learn by downweighting irrelevant evidence

Eight-month-old infants meta-learn by downweighting irrelevant evidence

Tracking Action in XR Mark Test Reveals Developing Body Representation Among Toddlers

Applications of Pose Estimation in Human Health and Performance across the Lifespan

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