Child Action Recognition in RGB and RGB-D Data

Turarova, Aizada; Zhanatkyzy, Aida; Telisheva, Zhansaule; Sabyrov, Arman; Sandygulova, Anara

doi:10.1145/3371382.3378391

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…With respect to ecological validity, incorporating inertial sensors into toys or other objects provides a means of freeing participants from potential MT constraints and can even be used in natural settings, such as daycares (e.g., Campolo et al, 2012). Furthermore, through advances in image processing, kinematics can be retrieved from video recordings, freeing participants from all measurement equipment (Turarova et al, 2020;Wei and Kording, 2018). Taken together, the contributions and applications of various types of MT within developmental research are substantial.…”

Section: Experimental and Ecological Validitymentioning

confidence: 99%

“…In a comparison study, 80% of OpenPose position errors were less than 30 mm, making it an interesting alternative to, but still considerably less accurate than, optical MT (Nakano et al, 2020). Several studies have applied OpenPose to video data of children (Suzuki et al, 2019;Turarova et al, 2020), while others have used it to quantify developmental changes in visual social input (Sanchez et al, 2018).…”

Section: Markerless Motion Trackingmentioning

confidence: 99%

See 1 more Smart Citation

Motion tracking in developmental research: Methods, considerations, and applications

Schaik

Dominici

2020

Progress in Brain Research

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Section: Experimental and Ecological Validitymentioning

confidence: 99%

Section: Markerless Motion Trackingmentioning

confidence: 99%

Motion tracking in developmental research: Methods, considerations, and applications

Schaik

Dominici

2020

Progress in Brain Research

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“…Meanwhile, the majority of existing action recognition databases have been designed for adults. There have been many studies related to children's action cognition—such as an infant action database including 18 actions extracted from Instagram and YouTube [ 12 ], action recognition including 7 actions in red, green, and blue (RGB) for children aged 6 to 11 years[ 13 ], and skeleton-driven action recognition including 6 actions for 32 children aged 6 to 9 years [ 14 ]—but there is no dataset that can be used publicly since they are all individual studies with minimal datasets or involve privacy issues.…”

Section: Introductionmentioning

confidence: 99%

Multiview child motor development dataset for AI-driven assessment of child development

Kim

Jang

et al. 2022

GigaScience

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Background Children's motor development is a crucial tool for assessing developmental levels, identifying developmental disorders early, and taking appropriate action. Although the Korean Developmental Screening Test for Infants and Children (K-DST) can accurately assess childhood development, its dependence on parental surveys rather than reliable, professional observation limits it. This study constructed a dataset based on a skeleton of recordings of K-DST behaviors in children aged between 20 and 71 months, with and without developmental disorders. The dataset was validated using a child behavior artificial intelligence (AI) learning model to highlight its possibilities. Results The 339 participating children were divided into 3 groups by age. We collected videos of 4 behaviors by age group from 3 different angles and extracted skeletons from them. The raw data were used to annotate labels for each image, denoting whether each child performed the behavior properly. Behaviors were selected from the K-DST's gross motor section. The number of images collected differed by age group. The original dataset underwent additional processing to improve its quality. Finally, we confirmed that our dataset can be used in the AI model with 93.94%, 87.50%, and 96.31% test accuracy for the 3 age groups in an action recognition model. Additionally, the models trained with data including multiple views showed the best performance. Conclusion Ours is the first publicly available dataset that constitutes skeleton-based action recognition in young children according to the standardized criteria (K-DST). This dataset will enable the development of various models for developmental tests and screenings.

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“…While this configuration of specific measurement was utilized to track human beings [6], to our knowledge and based on the conducted literature review, it was not previously studied in the context of human-robot interaction and collaboration. Some researcher [7][8][9][10][11][12][13][14][15] analyzed the literature review and found that most of the RGB-D use was meant for human identification and tracking, human activity recognition, human behavior analysis for shopping and security purposes, intelligent health care systems, detecting defects in produce and animal recognition, also a data-based had been developed to summarize all these uses and algorithms. It was proven that the top-view RGB-D cameras can be utilized successfully in several applications where behaviors and interactions can be analyzed and they are very attractive due to their affordability and the sufficient information extracted from the provided pictures or live feed.…”

Section: Introductionmentioning

confidence: 99%

Human-Robot Interaction and Collaboration (HRI-C) Utilizing Top-View RGB-D Camera System

Tashtoush¹,

Garcia²,

Landa³

et al. 2021

IJACSA

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In this study, a smart and affordable system that utilizes an RGB-D camera to measure the exact position of an operator with respect to an adjacent robotic manipulator was developed. This developed technology was implemented in a simulated human operation in an automated manufacturing robot to achieve two goals; enhancing the safety measures around the robot by adding an affordable smart system for human detection and robot control and developing a system that will allow the between the human-robot collaboration to finish a predefined task. The system utilized an Xbox Kinect V2 sensor/camera and Scorbot ER-V Plus to model and mimics the selected applications. To achieve these goals, a geometric model for the Scorbot and Xbox Kinect V2 was developed, a robotics joint calibration was applied, an algorithm of background segmentation was utilized to detect the operator and a dynamic binary mask for the robot was implemented, and the efficiency of both systems based on the response time and localization error was analyzed. The first application of the Add-on Safety Device aims to monitor the working-space and control the robot to avoid any collisions when an operator enters or gets closer. This application will reduced and remove physical barriers around the robots, expand the physical work area, reduce the proximity limitations, and enhance the human-robots interaction (HRI) in an industrial environment while sustaining a low cost. The system was able to respond to human intrusion to prevent any collision within 500 ms on average, and it was found that the system's bottleneck was PC and robot inter-communication speed. The second application was developing a successful collaborative scenario between a robot and a human operator, where a robot will deposit an object on the operator's hand, mimicking a real-life human-robot collaboration (HRC) tasks. The system was able to detect the operator's hand and it's location then command the robot to place an object on the hand, the system was able to place the object within a mean error of 2.4 cm, and the limitation of this system was the internal variables and data transmitting speed between the robot controller and main computer. These results are encouraging and ongoing work aims to experiment with different operations and implement gesture detection in real-time collaboration tasks while keeping the human operator safe and predicting their behavior.

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Child Action Recognition in RGB and RGB-D Data

Cited by 3 publications

References 6 publications

Motion tracking in developmental research: Methods, considerations, and applications

Motion tracking in developmental research: Methods, considerations, and applications

Multiview child motor development dataset for AI-driven assessment of child development

Human-Robot Interaction and Collaboration (HRI-C) Utilizing Top-View RGB-D Camera System

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