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A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.

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Related Experiment Video

Updated: Nov 10, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

A Deep Learning Framework for Recognizing Both Static and Dynamic Gestures.

Osama Mazhar^1,2, Sofiane Ramdani¹, Andrea Cherubini¹

¹LIRMM, Université de Montpellier, CNRS, 34392 Montpellier, France.

Sensors (Basel, Switzerland)

|April 3, 2021

Summary

This summary is machine-generated.

This study introduces StaDNet, a novel framework for recognizing static and dynamic human gestures using only RGB vision. This cost-effective approach enhances human-robot interaction in various settings.

Keywords:

commercial robots and applications cyber-physical systems gestures recognition human activity recognition operator interfaces

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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Area of Science:

Computer Vision
Human-Computer Interaction
Machine Learning

Background:

Intuitive user interfaces are crucial for human-centric smart environments.
Existing gesture recognition systems often require depth sensing, limiting their applicability and increasing costs.
There is a need for robust, inexpensive gesture recognition solutions for social and industrial human-robot interaction.

Purpose of the Study:

To propose a unified framework, StaDNet (Static and Dynamic gestures Network), for recognizing both static and dynamic gestures.
To enable gesture recognition using only simple RGB vision, eliminating the need for depth sensing.
To achieve state-of-the-art performance in human gesture recognition for improved human-robot interaction.

Main Methods:

Developed a pose-driven spatial attention strategy to guide the StaDNet.
Estimated human depth and identified regions-of-interest around hands from RGB images.
Utilized a fine-tuned Convolutional Neural Network (CNN) for static gesture detection and hand image-embedding extraction.
Fused hand image-embeddings with an augmented pose vector and processed through stacked Long Short-Term Memory (LSTM) blocks for dynamic gesture prediction.

Main Results:

The proposed StaDNet framework achieved superior performance compared to state-of-the-art methods on the large-scale Chalearn 2016 dataset.
Knowledge transfer of the learned methodology to the Praxis gestures dataset also resulted in outperforming existing state-of-the-art approaches.
The system successfully detects 10 static gestures per hand and predicts dynamic gestures using aggregated frame-wise information.

Conclusions:

The proposed StaDNet framework offers an effective and cost-efficient solution for human gesture recognition using RGB vision.
The pose-driven spatial attention mechanism and fusion of pose vectors with hand embeddings are key to the system's success.
This approach significantly advances the capabilities of human-robot interaction in smart environments.