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Human Pose Estimation and Tracking Though Cutting Edge Movement Sensors

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Dr. Amaia Méndez-Zorrilla

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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

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Guest Editor

Health PASS Research Group,Faculty of Psychology and Education, University of Deusto, 48007 Bizkaia, Spain
Interests: health and physical activity quantification
Special Issues, Collections and Topics in MDPI journals

Dr. Tyler Quinn

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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

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Research

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12 pages, 9350 KiB

Open AccessArticle

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://doi.org/10.3390/s22186850 - 10 Sep 2022

Cited by 12 | Viewed by 5051

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 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^{\circ}

for 3DDFA_V2 depending on the plane of movement, while the mean error reaches

14 . 1^{\circ}

and

11 . 0^{\circ}

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

Open AccessReview

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://doi.org/10.3390/s21185996 - 7 Sep 2021

Cited by 55 | Viewed by 11277

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 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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