The Visual Perception of Biological Motion in Adults

Hemeren, Paul; Rybarczyk, Yves

doi:10.1007/978-3-030-46732-6_4

Cited by 2 publications

(3 citation statements)

References 60 publications

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“…The previous research [ 1 , 2 , 3 ] highlights that to recognize actions, the human brain analyzes two levels of visual processing: one high level and one low level. The high-level processing deals with semantic processing and is based on global features, and the low-level processing is focused on kinematic features.…”

Section: Related Workmentioning

confidence: 99%

Fast Temporal Graph Convolutional Model for Skeleton-Based Action Recognition

Nan

Florea

2022

Sensors

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Human action recognition has a wide range of applications, including Ambient Intelligence systems and user assistance. Starting from the recognized actions performed by the user, a better human–computer interaction can be achieved, and improved assistance can be provided by social robots in real-time scenarios. In this context, the performance of the prediction system is a key aspect. The purpose of this paper is to introduce a neural network approach based on various types of convolutional layers that can achieve a good performance in recognizing actions but with a high inference speed. The experimental results show that our solution, based on a combination of graph convolutional networks (GCN) and temporal convolutional networks (TCN), is a suitable approach that reaches the proposed goal. In addition to the neural network model, we design a pipeline that contains two stages for obtaining relevant geometric features, data augmentation and data preprocessing, also contributing to an increased performance.

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Section: Related Workmentioning

confidence: 99%

Fast Temporal Graph Convolutional Model for Skeleton-Based Action Recognition

Nan

Florea

2022

Sensors

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“…This can be as simple as noticing that another person is looking at something interesting and looking at the same thing, or a shared activity such as collaborating on moving furniture through a doorway. This can be coupled with how humans perceive each other's intentions, often focusing on gauging the other person's motion rather than maintaining eye contact [33]. Human motion has been found to be identifiable even when only a grid of points is shown, not a whole human, and that this motion not only follows reasonably simple rules but that people also use the motion to gauge intentions [33,34].…”

Section: Collaborative Robot Applicationsmentioning

confidence: 99%

“…This can be coupled with how humans perceive each other's intentions, often focusing on gauging the other person's motion rather than maintaining eye contact [33]. Human motion has been found to be identifiable even when only a grid of points is shown, not a whole human, and that this motion not only follows reasonably simple rules but that people also use the motion to gauge intentions [33,34]. An important note is that some studies on this kind of motion identification or intention identification focus on general motion, while others focus on a context-specific motion such as e.g., identification of cyclists' intentions in traffic.…”

Section: Collaborative Robot Applicationsmentioning

confidence: 99%

A Review on Communicative Mechanisms of External HMIs in Human-Technology Interaction

Thorvald

Kolbeinsson

Fogelberg

2022

2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)

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The Operator 4.0 typology depicts the collaborative operator as one of eight operator working scenarios of operators in Industry 4.0. It signifies collaborative robot applications and the interaction between humans and robots working collaboratively or cooperatively towards a common goal. For this collaboration to run seamlessly and effortlessly, human-robot communication is essential. We briefly discuss what trust, predictability, and intentions are, before investigating the communicative features of both self-driving cars and collaborative robots. We found that although communicative external HMIs could arguably provide some benefits in both domains, an abundance of clues to what an autonomous car or a robot is about to do are easily accessible through the environment or could be created simply by understanding and designing legible motions.

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The Visual Perception of Biological Motion in Adults

Cited by 2 publications

References 60 publications

Fast Temporal Graph Convolutional Model for Skeleton-Based Action Recognition

Fast Temporal Graph Convolutional Model for Skeleton-Based Action Recognition

A Review on Communicative Mechanisms of External HMIs in Human-Technology Interaction

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