Deep Learning Applications on Human Activity Recognition
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 20 December 2025 | Viewed by 14
Special Issue Editors
Interests: point cloud analysis and registration; differential entropy analysis; machine vision; human pose estimation; deep learning application
Interests: discrete geometries; 3D scanning; geometric segmentation; augmented reality
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Human activity recognition (HAR) has become a key technology with transformative applications in healthcare, smart environments, security, sports analytics, and human–computer interaction. The adoption of deep learning has significantly enhanced HAR capabilities, enabling the more accurate and scalable recognition of human activities from sensor data, video streams, and multimodal sources.
This Special Issue aims to showcase cutting-edge applications of deep learning in HAR, emphasizing real-world implementations and their impact on various domains. We invite high-quality original research articles and comprehensive reviews that explore how deep learning is being leveraged to improve activity recognition in practical settings. Contributions that address challenges related to data collection, deployment in real-world scenarios, and integration with emerging technologies such as the IoT, wearable devices, and smart cities are particularly encouraged.
Topics of interest include, but are not limited to, the following:
- Industrial and workplace safety applications.
- Healthcare applications of HAR, including rehabilitation monitoring.
- Activity recognition in sports and fitness tracking.
- HAR in human–computer interaction and augmented reality.
- Real-time HAR applications in smart environments.
- HAR in autonomous systems and robotics.
- HAR for security and surveillance.
- Smart home and smart city applications using HAR.
- Sensor-based activity recognition using deep learning.
- Computer-vision-based HAR.
- Multimodal data fusion for HAR.
We welcome contributions that present novel applications, case studies, and implementations demonstrating the impact of deep learning on HAR across various domains.
Dr. Emmanuele Barberi
Dr. Emanuele Guardiani
Guest Editors
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Keywords
- human activity recognition
- deep learning applications
- computer vision
- healthcare and smart environments
- sports analytics
- wearable sensor technology
- real-time HAR
- security and surveillance
- IoT and smart cities
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