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Clinical Applications of Depth Cameras for Posture Analysis and Pose...
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Clinical Applications of Depth Cameras for Posture Analysis and Pose Tracking

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

Deadline for manuscript submissions: closed (20 May 2020) | Viewed by 7033

Special Issue Editors

Dr. Ross A. Clark

E-Mail Website
Guest Editor
School of Health and Sports Sciences, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD 4556, Australia
Interests: clinical motion analysis; balance assessment; outcome measures; rehabilitation
Dr. Benjamin Mentiplay

E-Mail Website
Guest Editor
La Trobe Sport and Exercise Medicine Research Centre, La Trobe University, Bundoora, VIC 3086, Australia
Interests: biomechanics; gait analysis; rehabilitation; low-cost technology
Dr. Yonghao Pua

E-Mail Website
Guest Editor
Associate Professor, Department of Physiotherapy, Singapore general hospital, Outram road, Singapore 169608, Singapore
Interests: statistical design; clinical outcome registries; prediction; physical function

Special Issue Information

Dear Colleagues,

Exciting, ground-breaking work has been performed in recent years to create low-cost, high-quality depth cameras. These devices can be used to create three dimensional maps, which can be used for a wide range of purposes including robotic guidance and vehicle automation, human posture analysis and tracking movement of objects and body parts in space. These cameras can be combined with skeleton pose tracking to create powerful tools for human and animal motion analysis, which has received significant attention for the potential healthcare benefits.

The aim of this special edition is to provide a highly visible, open access repository of work performed using depth cameras. Papers addressing this topic, particularly those that provide new insights into their use for human and animal-related posture and motion tracking, are welcome. Articles may include but are not limited to:

  • Robust validation studies of existing technology and/or software
  • Advances in machine learning algorithms to improve the accuracy of pose and motion tracking
  • Prediction models incorporating data from these devices and software to identify health outcomes

Dr. Ross A. Clark
Dr. Benjamin Mentiplay
Dr. Yonghao Pua
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.

Keywords

  • Kinect
  • pose tracking
  • skeleton tracking
  • depth camera
  • simultaneous localization and mapping
  • motion analysis
  • marker-less tracking

Published Papers (2 papers)

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Research

16 pages, 3195 KiB  
Article
Validation, Reliability, and Responsiveness Outcomes of Kinematic Assessment with an RGB-D Camera to Analyze Movement in Subacute and Chronic Low Back Pain
by Manuel Trinidad-Fernández, David Beckwée, Antonio Cuesta-Vargas, Manuel González-Sánchez, Francisco-Angel Moreno, Javier González-Jiménez, Erika Joos and Peter Vaes
Sensors 2020, 20(3), 689; https://0-doi-org.brum.beds.ac.uk/10.3390/s20030689 - 27 Jan 2020
Cited by 9 | Viewed by 3183
Abstract
Background: The RGB-D camera is an alternative to asses kinematics in order to obtain objective measurements of functional limitations. The aim of this study is to analyze the validity, reliability, and responsiveness of the motion capture depth camera in sub-acute and chronic low [...] Read more.
Background: The RGB-D camera is an alternative to asses kinematics in order to obtain objective measurements of functional limitations. The aim of this study is to analyze the validity, reliability, and responsiveness of the motion capture depth camera in sub-acute and chronic low back pain patients. Methods: Thirty subjects (18–65 years) with non-specific lumbar pain were screened 6 weeks following an episode. RGB-D camera measurements were compared with an inertial measurement unit. Functional tests included climbing stairs, bending, reaching sock, lie-to-sit, sit-to-stand, and timed up-and-go. Subjects performed the maximum number of repetitions during 30 s. Validity was analyzed using Spearman’s correlation, reliability of repetitions was calculated by the intraclass correlation coefficient and the standard error of measurement, and receiver operating characteristic curves were calculated to assess the responsiveness. Results: The kinematic analysis obtained variable results according to the test. The time variable had good values in the validity and reliability of all tests (r = 0.93–1.00, (intraclass correlation coefficient (ICC) = 0.62–0.93). Regarding kinematics, the best results were obtained in bending test, sock test, and sit-to-stand test (r = 0.53–0.80, ICC = 0.64–0.83, area under the curve (AUC) = 0.55–84). Conclusion: Functional tasks, such as bending, sit-to-stand, reaching, and putting on sock, assessed with the RGB-D camera, revealed acceptable validity, reliability, and responsiveness in the assessment of patients with low back pain (LBP). Trial registration: ClinicalTrials.gov NCT03293095 “Functional Task Kinematic in Musculoskeletal Pathology” 26 September 2017 Full article
(This article belongs to the Special Issue Clinical Applications of Depth Cameras for Posture Analysis and Pose Tracking)
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14 pages, 1628 KiB  
Article
Validation of a Single RGB-D Camera for Gait Assessment of Polyneuropathy Patients
by Maria do Carmo Vilas-Boas, Ana Patrícia Rocha, Hugo Miguel Pereira Choupina, Márcio Neves Cardoso, José Maria Fernandes, Teresa Coelho and João Paulo Silva Cunha
Sensors 2019, 19(22), 4929; https://0-doi-org.brum.beds.ac.uk/10.3390/s19224929 - 12 Nov 2019
Cited by 20 | Viewed by 3379
Abstract
Motion analysis systems based on a single markerless RGB-D camera are more suitable for clinical practice than multi-camera marker-based reference systems. Nevertheless, the validity of RGB-D cameras for motor function assessment in some diseases affecting gait, such as Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP), [...] Read more.
Motion analysis systems based on a single markerless RGB-D camera are more suitable for clinical practice than multi-camera marker-based reference systems. Nevertheless, the validity of RGB-D cameras for motor function assessment in some diseases affecting gait, such as Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP), is yet to be investigated. In this study, the agreement between the Kinect v2 and a reference system for obtaining spatiotemporal and kinematic gait parameters was evaluated in the context of TTR-FAP. 3-D body joint data provided by both systems were acquired from ten TTR-FAP symptomatic patients, while performing ten gait trials. For each gait cycle, we computed several spatiotemporal and kinematic gait parameters. We then determined, for each parameter, the Bland Altman’s bias and 95% limits of agreement, as well as the Pearson’s and concordance correlation coefficients, between systems. The obtained results show that an affordable, portable and non-invasive system based on an RGB-D camera can accurately obtain most of the studied gait parameters (excellent or good agreement for eleven spatiotemporal and one kinematic). This system can bring more objectivity to motor function assessment of polyneuropathy patients, potentially contributing to an improvement of TTR-FAP treatment and understanding, with great benefits to the patients’ quality of life. Full article
(This article belongs to the Special Issue Clinical Applications of Depth Cameras for Posture Analysis and Pose Tracking)
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