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Deep Learning Applications for Pose Estimation and Human Action Recognition—2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 1096

Special Issue Editors


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Guest Editor
Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy
Interests: deep learning; machine learning; computer vision; depth estimation; attitude and pose estimation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Florence, Via S. Marta 3, 50139 Florence, Italy
Interests: navigation and positioning; attitude and pose estimation; 3D modeling; geomatics; sensors; deep learning; computer vision; climate change; cultural heritage preservation; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, deep learning has drawn significant attention thanks to its robustness and potential in generalization and learning capabilities. Several applications have been tested and successfully deployed, exploring the majority of real-world tasks with the aim of improving their performances. Among others, pose estimation and human action recognition have benefitted from the exceptional results achieved in the deep learning field, although still showing wide margins of improvement.

This Special Issue aims to gather a significant collection of original contributions to these topics. Accurate vehicle and human pose estimation is crucial for several applications, e.g., animal behavior research, gaming and virtual reality, medicine and biotechnology, pedestrian, aerial and maritime navigation, robotics, and human motion tracking. Furthermore, effective human pose and action recognition offers an important contribution in many fields, such as physical therapists’ diagnoses and patient rehabilitation, as well as security and surveillance or employee-free store development.

The relevant topics of this issue include, but are not limited to, the following:

  • Single and multihuman pose estimation, action recognition, and tracking;
  • Terrestrial, maritime, aerial robot pose estimation, and tracking;
  • Literature reviews and surveys;
  • Datasets and sensors;
  • Interesting applications and ideas focusing on surveillance, autonomous navigation, human–robot interaction, healthcare, and sports, etc.

Dr. Paolo Russo
Dr. Fabiana Di Ciaccio
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • action recognition
  • pose estimation
  • human activities
  • robotics and intelligent systems
  • navigation
  • positioning
  • control
  • datasets
  • sensors
  • embedded systems and devices

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Published Papers (1 paper)

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Research

16 pages, 2388 KiB  
Article
Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition
by Domonkos Varga
Sensors 2024, 24(24), 8201; https://doi.org/10.3390/s24248201 - 22 Dec 2024
Viewed by 914
Abstract
Human action recognition using WiFi channel state information (CSI) has gained attention due to its non-intrusive nature and potential applications in healthcare, smart environments, and security. However, the reliability of methods developed for CSI-based action recognition is often contingent on the quality of [...] Read more.
Human action recognition using WiFi channel state information (CSI) has gained attention due to its non-intrusive nature and potential applications in healthcare, smart environments, and security. However, the reliability of methods developed for CSI-based action recognition is often contingent on the quality of the datasets and evaluation protocols used. In this paper, we uncovered a critical data leakage issue, which arises from improper data partitioning, in a widely used WiFi CSI benchmark dataset. Specifically, the benchmark fails to separate individuals between the training and test sets, leading to inflated performance metrics as models inadvertently learn individual-specific features rather than generalizable action patterns. We analyzed this issue in depth, retrained several benchmarked models using corrected data partitioning methods, and demonstrated a significant drop in accuracy when individuals were properly separated across training and testing. Our findings highlight the importance of rigorous data partitioning in CSI-based action recognition and provide recommendations for mitigating data leakage in future research. This work contributes to the development of more robust and reliable human action recognition systems using WiFi CSI. Full article
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