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Related Experiment Video

Updated: Jan 11, 2026

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

Human-Machine Collaborative Learning for Streaming Data-Driven Scenarios.

Fan Yang^1,2, Xiaojuan Zhang^1,2, Zhiwen Yu³

¹School of Computer Science, Qinghai Normal University, Xining 810016, China.

Sensors (Basel, Switzerland)

|November 13, 2025

Summary

This summary is machine-generated.

Human-machine collaboration enhances deep learning by combining human expertise with machine efficiency. This approach improves accuracy and robustness in complex tasks, especially with limited data.

Keywords:

deep learning human intelligence human–machine collaborative learning streaming data-driven

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Area of Science:

Artificial Intelligence
Machine Learning
Human-Computer Interaction

Background:

Deep learning excels but struggles with limited training data and ambiguous outputs.
Traditional methods lack adaptability in dynamic, untrustworthy environments.
Integrating human intelligence with machine computing offers a synergistic solution.

Purpose of the Study:

To propose a novel framework integrating human and machine intelligence for complex data-driven tasks.
To leverage the strengths of both humans (decision-making, feedback) and machines (computation).
To enhance accuracy and provide clearer explanations in challenging scenarios.

Main Methods:

A flexible, interactive human-machine cooperation framework was developed.
Humans handled decisive tasks and provided empirical feedback.
Machines performed repetitive computations and pattern recognition.

Main Results:

The framework demonstrated significantly improved accuracy in video anomaly detection, person re-identification, and sound event detection.
Human knowledge fusion with deep learning processing led to confirmed final decisions.
Competitive performance was achieved with minimal human intervention.

Conclusions:

The proposed human-machine collaborative learning mechanism offers a robust approach for machine learning.
This novel framework is particularly effective in dynamic and untrustworthy conditions.
It represents a new paradigm in machine learning, enhancing performance through synergistic intelligence.