MIDL 2021

 
International Conference on
Medical Imaging with Deep Learning
5. bis 7. Juli 2021

HRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints

TitleHRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints
Publication TypeJournal Article
Year of Publication2021
AuthorsBüker L.C., Zuber F., Hein A., Fudickar S.
JournalSensors
Volume21
Issue4
Date Published2021
Publication Languageeng
ISSN1424-8220
Abstract

With approaches for the detection of joint positions in color images such as HRNet and OpenPose being available, consideration of corresponding approaches for depth images is limited even though depth images have several advantages over color images like robustness to light variation or color- and texture invariance. Correspondingly, we introduce High- Resolution Depth Net (HRDepthNet)—a machine learning driven approach to detect human joints (body, head, and upper and lower extremities) in purely depth images. HRDepthNet retrains the original HRNet for depth images. Therefore, a dataset is created holding depth (and RGB) images recorded with subjects conducting the timed up and go test—an established geriatric assessment. The images were manually annotated RGB images. The training and evaluation were conducted with this dataset. For accuracy evaluation, detection of body joints was evaluated via COCO’s evaluation metrics and indicated that the resulting depth image-based model achieved better results than the HRNet trained and applied on corresponding RGB images. An additional evaluation of the position errors showed a median deviation of 1.619 cm (x-axis), 2.342 cm (y-axis) and 2.4 cm (z-axis).

URLhttps://www.mdpi.com/1424-8220/21/4/1356
DOI10.3390/s21041356
Created at November 22, 2021 - 2:56pm by Fudickar.

Languages

Program of Study

Study Medical Informatics
at the University of Lübeck

read more ...

Address

Office
Susanne Petersen

Tel+49 451 3101 5601
Fax+49 451 3101 5604


Ratzeburger Allee 160
23538 Lübeck
Germany