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Human Pose Estimation and Tracking Though Cutting Edge Movement Sensors
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Human Pose Estimation and Tracking Though Cutting Edge Movement Sensors

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

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 12140

Special Issue Editors

Dr. Amaia Méndez-Zorrilla

E-Mail Website
Guest Editor
The Deustotech-LIFE (eVIDA) Research Group, Faculty of Engineering, University of Deusto, 48007 Bilbao, Spain
Interests: new technologies applied to physical activity; health and wellbeing; image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Aitor Coca

E-Mail Website
Guest Editor
Health PASS Research Group,Faculty of Psychology and Education, University of Deusto, 48007 Bizkaia, Spain
Interests: health and physical activity quantification
Dr. Tyler Quinn

E-Mail Website
Guest Editor
National Personal Protective Technology Laboratory (NPPTL), National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and Prevention (CDC), Atlanta, GA 30341, USA
Interests: 24-hour objective physical activity measurement; occupational physical activity measurement; accelerometry; inclinometry; ambulatory cardiovascular monitoring

Special Issue Information

Dear Colleagues,

The population's concern for maintaining healthy living habits has increased. This has led to an increased interest in physical activity, which has been widely shown to improve our health and quality of life.

At the same time, the rapid advance of technology and the reduction in the cost of hardware has led to major innovations in sensors aimed at detecting, tracking, and estimating movement.

This is an opportunity for the study of posture and movement during physical activity, in sports professionals, in people recovering from an injury or illness, and in special populations (children and older adults).

This Special Issue, entitled “Human Pose Estimation and Tracking though Cutting-Edge Movement Sensors” will focus, but not be limited to, the following main topics:

  • State-of-the-art and next-generation movement sensors related to the field of physical activity;
  • 3D human pose estimation;
  • Pose reconstruction;
  • Gesture tracking;
  • Sensor network configurations;
  • Methodological advancement in 24-hour physical activity monitoring.

Dr. Amaia Méndez-Zorrilla
Prof. Dr. Aitor Coca
Dr. Tyler Quinn
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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.

Published Papers (2 papers)

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Research

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12 pages, 9350 KiB  
Article
Evaluation of Various State of the Art Head Pose Estimation Algorithms for Clinical Scenarios
by Yassine Hammadi, François Grondin, François Ferland and Karina Lebel
Sensors 2022, 22(18), 6850; https://0-doi-org.brum.beds.ac.uk/10.3390/s22186850 - 10 Sep 2022
Cited by 5 | Viewed by 3493
Abstract
Head pose assessment can reveal important clinical information on human motor control. Quantitative assessment have the potential to objectively evaluate head pose and movements’ specifics, in order to monitor the progression of a disease or the effectiveness of a treatment. Optoelectronic camera-based motion-capture [...] Read more.
Head pose assessment can reveal important clinical information on human motor control. Quantitative assessment have the potential to objectively evaluate head pose and movements’ specifics, in order to monitor the progression of a disease or the effectiveness of a treatment. Optoelectronic camera-based motion-capture systems, recognized as a gold standard in clinical biomechanics, have been proposed for head pose estimation. However, these systems require markers to be positioned on the person’s face which is impractical for everyday clinical practice. Furthermore, the limited access to this type of equipment and the emerging trend to assess mobility in natural environments support the development of algorithms capable of estimating head orientation using off-the-shelf sensors, such as RGB cameras. Although artificial vision is a popular field of research, limited validation of human pose estimation based on image recognition suitable for clinical applications has been performed. This paper first provides a brief review of available head pose estimation algorithms in the literature. Current state-of-the-art head pose algorithms designed to capture the facial geometry from videos, OpenFace 2.0, MediaPipe and 3DDFA_V2, are then further evaluated and compared. Accuracy is assessed by comparing both approaches to a baseline, measured with an optoelectronic camera-based motion-capture system. Results reveal a mean error lower or equal to 5.6∘ for 3DDFA_V2 depending on the plane of movement, while the mean error reaches 14.1∘ and 11.0∘ for OpenFace 2.0 and MediaPipe, respectively. This demonstrates the superiority of the 3DDFA_V2 algorithm in estimating head pose, in different directions of motion, and suggests that this algorithm can be used in clinical scenarios. Full article
(This article belongs to the Special Issue Human Pose Estimation and Tracking Though Cutting Edge Movement Sensors)
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Review

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25 pages, 1491 KiB  
Review
A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise
by Aritz Badiola-Bengoa and Amaia Mendez-Zorrilla
Sensors 2021, 21(18), 5996; https://0-doi-org.brum.beds.ac.uk/10.3390/s21185996 - 7 Sep 2021
Cited by 30 | Viewed by 7584
Abstract
Human Pose Estimation (HPE) has received considerable attention during the past years, improving its performance thanks to the use of Deep Learning, and introducing new interesting uses, such as its application in Sport and Physical Exercise (SPE). The aim of this systematic review [...] Read more.
Human Pose Estimation (HPE) has received considerable attention during the past years, improving its performance thanks to the use of Deep Learning, and introducing new interesting uses, such as its application in Sport and Physical Exercise (SPE). The aim of this systematic review is to analyze the literature related to the application of HPE in SPE, the available data, methods, performance, opportunities, and challenges. One reviewer applied different inclusion and exclusion criteria, as well as quality metrics, to perform the paper filtering through the paper databases. The Association for Computing Machinery Digital Library, Web of Science, and dblp included more than 500 related papers after the initial filtering, finally resulting in 20. In addition, research was carried out regarding the publicly available data related to this topic. It can be concluded that even if related public data can be found, much more data is needed to be able to obtain good performance in different contexts. In relation with the methods of the authors, the use of general purpose systems as base, such as Openpose, combined with other methods and adaptations to the specific use case can be found. Finally, the limitations, opportunities, and challenges are presented. Full article
(This article belongs to the Special Issue Human Pose Estimation and Tracking Though Cutting Edge Movement Sensors)
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