Deep Learning-Based Pose Estimation: Applications in Vision, Robotics, and Beyond
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: 31 December 2025 | Viewed by 305
Special Issue Editors
2. Department of Mechanical Engineering, Indian Institute of Technology Patna, Patna 801106, Bihar, India
Interests: deep learning; large language models; time series forecasting; clinical pose estimation; neurobehavioral data analysis; predictive modeling; human–robot interaction
2. Institute for Artificial and Human Intelligence, University of Bayreuth, 95445 Bayreuth, Germany
Interests: wearable system; biosensors; biomarkers; large language models; human sensory and motor control; brain–computer interface; software engineering; machine learning
Special Issue Information
Dear Colleagues,
Deep learning has brought about a transformative change in many industries by giving machines the ability to identify and analyze intricate patterns in large datasets. This advancement is particularly evident in fields like computer vision and robotics, where deep learning-based pose estimation has become an essential tool. Pose estimation refers to the process of determining the position and orientation of objects or people within a given space. Traditional approaches to pose estimation often struggled with significant challenges such as dealing with occlusions—where parts of an object are hidden from view—and adapting to varying environmental conditions, such as changes in lighting or background. However, deep learning has revolutionized this area by offering a more robust and precise method for pose estimation. With the ability to learn from vast amounts of data, deep learning models can generalize well across different scenarios, overcoming the limitations of traditional methods. For instance, in human–computer interaction, accurate pose estimation enables more natural and intuitive interfaces, while in sports analytics, it allows for detailed analysis of athletes' movements, leading to improved performance and injury prevention strategies. In robotics, deep learning-based pose estimation is essential for tasks that require machines to interact with the physical world, such as navigating environments, manipulating objects, or collaborating with humans. The significance of this technology extends to healthcare as well, particularly in the development of intelligent prosthetics and exoskeletons. These devices require precise control over movement to be effective, and deep learning's ability to accurately estimate poses plays a vital role in achieving this. By integrating deep learning with pose estimation, we not only improve existing technologies but also pave the way for new, AI-driven innovations that have the potential to impact a wide range of fields.
This Special Issue aims to explore the breakthrough of deep learning in industries where the identification and analysis of complex patterns in large datasets are critical. Specifically, it will focus on the role of deep learning in advancing pose estimation technologies across various domains such as computer vision, robotics, healthcare, and human-computer interaction. The issue invites contributions that highlight cutting-edge research and applications where deep learning's ability to handle challenges like occlusions and varying conditions has led to significant improvements in accuracy and robustness.
This Special Issue delves into the intersection of deep learning, big data, and cognitive computing—the core focus areas of the journal. The integration of deep learning with pose estimation represents a quintessential application of cognitive computing, where machines mimic human-like understanding and decision-making processes. Moreover, the reliance on large datasets for training deep learning models aligns perfectly with the journal's emphasis on big data. This Special Issue will thus contribute valuable insights to the journal's readership, enhancing their understanding of how deep learning is shaping the future of big data analytics and cognitive computing.
Here are some potential themes for the Special Issue:
- Achieving accurate pose estimation using big data and intelligent techniques.
- Enhancing user experience in human–computer interaction with advanced pose estimation techniques.
- Enabling robots to navigate complex environments with improved pose estimation.
- Advancing intelligent prosthetics and exoskeletons through healthcare applications of pose estimation.
- Integrating artificial intelligence and human perception through cognitive computing and pose estimation.
- Athletic performance and ensuring safety with pose estimation in sports analytics.
- Addressing ethical and privacy considerations in pose estimation to balance innovation with responsibility.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Deep Learning;
- Pose Estimation;
- Computer Vision;
- Robotics;
- Human-Computer Interaction;
- Big Data Analytics;
- Cognitive Computing;
- Intelligent Prosthetics;
- Exoskeletons;
- Sports Analytics;
- Machine Learning;
- Biometrics;
- Occlusion Handling;
- Environmental Adaptation;
- Pattern Recognition;
- Movement Analysis;
- Data Generalization;
- Healthcare Technology;
- Human-Robot Collaboration.
We look forward to receiving your contributions.
Dr. Jyotindra Narayan
Dr. Chaiyawan Auepanwiriyakul
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning
- pose estimation
- computer vision
- human-computer interaction
- big data analytics
- sports analytics
- machine learning
- biometrics
- occlusion handling
- pattern recognition
- movement analysis
- data generalization
- healthcare technology
- human-robot collaboration
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