{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T15:06:26Z","timestamp":1761491186695,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,14]],"date-time":"2021-02-14T00:00:00Z","timestamp":1613260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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)\u2014a 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\u2014an 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\u2019s 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).<\/jats:p>","DOI":"10.3390\/s21041356","type":"journal-article","created":{"date-parts":[[2021,2,14]],"date-time":"2021-02-14T10:01:05Z","timestamp":1613296865000},"page":"1356","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["HRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints"],"prefix":"10.3390","volume":"21","author":[{"given":"Linda Christin","family":"B\u00fcker","sequence":"first","affiliation":[{"name":"Assistance Systems and Medical Device Technology, Department of Health Services Research, Carl von Ossietzky University Oldenburg, 26129 Oldenburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Finnja","family":"Zuber","sequence":"additional","affiliation":[{"name":"Assistance Systems and Medical Device Technology, Department of Health Services Research, Carl von Ossietzky University Oldenburg, 26129 Oldenburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8846-2282","authenticated-orcid":false,"given":"Andreas","family":"Hein","sequence":"additional","affiliation":[{"name":"Assistance Systems and Medical Device Technology, Department of Health Services Research, Carl von Ossietzky University Oldenburg, 26129 Oldenburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3553-5131","authenticated-orcid":false,"given":"Sebastian","family":"Fudickar","sequence":"additional","affiliation":[{"name":"Assistance Systems and Medical Device Technology, Department of Health Services Research, Carl von Ossietzky University Oldenburg, 26129 Oldenburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Merriaux, P., Dupuis, Y., Boutteau, R., Vasseur, P., and Savatier, X. (2017). A Study of Vicon System Positioning Performance. Sensors, 17.","DOI":"10.3390\/s17071591"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1590\/bjpt-rbf.2014.0067","article-title":"Spatiotemporal gait parameters and recurrent falls in community-dwelling elderly women: A prospective study","volume":"19","author":"Moreira","year":"2015","journal-title":"Braz. J. Phys. Ther."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bueno, G.A.S., Gerv\u00e1sio, F.M., Ribeiro, D.M., Martins, A.C., Lemos, T.V., and de Menezes, R.L. (2019). Fear of Falling Contributing to Cautious Gait Pattern in Women Exposed to a Fictional Disturbing Factor: A Non-randomized Clinical Trial. Front. Neurol., 10.","DOI":"10.3389\/fneur.2019.00283"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.medengphy.2017.03.007","article-title":"Microsoft Kinect can distinguish differences in over-ground gait between older persons with and without Parkinson\u2019s disease","volume":"44","author":"Eltoukhy","year":"2017","journal-title":"Med. Eng. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Leu, A., Risti\u0107-Durrant, D., and Gr\u00e4ser, A. (2011, January 19\u201321). A robust markerless vision-based human gait analysis system. Proceedings of the 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania.","DOI":"10.1109\/SACI.2011.5873039"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"186780","DOI":"10.1155\/2015\/186780","article-title":"A 2D Markerless Gait Analysis Methodology: Validation on Healthy Subjects","volume":"2015","author":"Castelli","year":"2015","journal-title":"Comput. Math. Methods Med."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Fudickar, S., Hellmers, S., Lau, S., Diekmann, R., Bauer, J.M., and Hein, A. (2020). Measurement System for Unsupervised Standardized Assessment of Timed \u201cUp & Go\u201d and Five Times Sit to Stand Test in the Community\u2014A Validity Study. Sensors, 20.","DOI":"10.3390\/s20102824"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hellmers, S., Izadpanah, B., Dasenbrock, L., Diekmann, R., Bauer, J.M., Hein, A., and Fudickar, S. (2018). Towards an automated unsupervised mobility assessment for older people based on inertial TUG measurements. Sensors, 18.","DOI":"10.3390\/s18103310"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dubois, A., Bihl, T., and Bresciani, J.P. (2018). Automating the Timed Up and Go Test Using a Depth Camera. Sensors, 18.","DOI":"10.3390\/s18010014"},{"key":"ref_10","first-page":"1","article-title":"Description of spatio-temporal gait parameters in elderly people and their association with history of falls: Results of the population-based cross-sectional KORA-Age study","volume":"15","author":"Peters","year":"2015","journal-title":"BMC Geriatr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2605","DOI":"10.1111\/jgs.16135","article-title":"Validation of a Multi\u2013Sensor-Based Kiosk for Short Physical Performance Battery","volume":"67","author":"Jung","year":"2019","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hellmers, S., Fudickar, S., Lau, S., Elgert, L., Diekmann, R., Bauer, J.M., and Hein, A. (2019). Measurement of the Chair Rise Performance of Older People Based on Force Plates and IMUs. Sensors, 19.","DOI":"10.3390\/s19061370"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lu, Y., Vincent, N., Yuen, P.C., Zheng, W.S., Cheriet, F., and Suen, C.Y. (2020). Sit-to-Stand Test for Neurodegenerative Diseases Video Classification. Pattern Recognition and Artificial Intelligence, Springer International Publishing.","DOI":"10.1007\/978-3-030-59830-3"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yang, C., Ugbolue, U.C., Kerr, A., Stankovic, V., Stankovic, L., Carse, B., Kaliarntas, K.T., and Rowe, P.J. (2016). Autonomous gait event detection with portable single-camera gait kinematics analysis system. J. Sens., 2016.","DOI":"10.1155\/2016\/5036857"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Arizpe-Gomez, P., Harms, K., Fudickar, S., Janitzky, K., Witt, K., and Hein, A. (2020, January 15\u201318). Preliminary Viability Test of a 3-D-Consumer-Camera-Based System for Automatic Gait Feature Detection in People with and without Parkinson\u2019s Disease. Proceedings of the ICHI 2020, Oldenburg, Germany.","DOI":"10.1109\/ICHI48887.2020.9374363"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","article-title":"OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields","volume":"43","author":"Cao","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 25). Deep High-Resolution Representation Learning for Human Pose Estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_18","unstructured":"Lin, T.Y., Patterson, G., Ronchi, M.R., Cui, Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., and Perona, P. (2020, April 16). COCO\u2014Common Objects in Context\u2014Keypoint Evaluation. Available online: http:\/\/cocodataset.org\/#keypoints-eval."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Veges, M., and Lorincz, A. (2020). Multi-Person Absolute 3D Human Pose Estimation with Weak Depth Supervision. arXiv.","DOI":"10.1007\/978-3-030-61609-0_21"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ye, M., Wang, X., Yang, R., Ren, L., and Pollefeys, M. (2011, January 6\u201311). Accurate 3d pose estimation from a single depth image. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126310"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ganapathi, V., Plagemann, C., Koller, D., and Thrun, S. (2010, January 13\u201318). Real time motion capture using a single time-of-flight camera. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540141"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., and Blake, A. (2011, January 20\u201325). Real-time human pose recognition in parts from single depth images. Proceedings of the CVPR 2011, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995316"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wei, X., Zhang, P., and Chai, J. (2012). Accurate Realtime Full-Body Motion Capture Using a Single Depth Camera. ACM Trans. Graph., 31.","DOI":"10.1145\/2366145.2366207"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Park, S., Yu, S., Kim, J., Kim, S., and Lee, S. (2012). 3D hand tracking using Kalman filter in depth space. EURASIP J. Adv. Signal Process., 2012.","DOI":"10.1186\/1687-6180-2012-36"},{"key":"ref_25","unstructured":"Cremers, D., Reid, I., Saito, H., and Yang, M.H. (2015). Regularity Guaranteed Human Pose Correction. Computer Vision\u2014ACCV 2014, Springer International Publishing."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., and Cousins, S. (2011, January 9\u201313). 3D is here: Point Cloud Library (PCL). Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980567"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mederos, B., Velho, L., and De Figueiredo, L.H. (2003, January 12\u201315). Moving least squares multiresolution surface approximation. Proceedings of the 16th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2003), Sao Carlos, Brazil.","DOI":"10.1109\/SIBGRA.2003.1240987"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hartley, R., and Zisserman, A. (2004). Multiple View Geometry in Computer Vision, Cambridge University Press.","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_29","unstructured":"Yodayoda (2021, February 05). From Depth Map to Point Cloud. Available online: https:\/\/medium.com\/yodayoda\/from-depth-map-to-point-cloud-7473721d3f."},{"key":"ref_30","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA. Conference Track Proceedings."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Obdrzalek, S., Kurillo, G., Ofli, F., Bajcsy, R., Seto, E., Jimison, H., and Pavel, M. (September, January 28). Accuracy and robustness of Kinect pose estimation in the context of coaching of elderly population. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6346149"},{"key":"ref_32","unstructured":"Association WMA (2013). WMA Deklaration von Helsinki\u2014Ethische Grunds\u00e4tze f\u00fcr die Medizinische Forschung am Menschen, WMA."},{"key":"ref_33","unstructured":"Dutta, A., Gupta, A., and Zissermann, A. (2019, July 14). VGG Image Annotator (VIA). Available online: http:\/\/www.robots.ox.ac.uk\/~vgg\/software\/via\/."},{"key":"ref_34","unstructured":"Lin, T.Y., Patterson, G., Ronchi, M.R., Cui, Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., and Perona, P. (2020, April 16). COCO\u2014Common Objects in Context\u2014What Is COCO?. Available online: http:\/\/cocodataset.org\/#home."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/MRA.2018.2852795","article-title":"An Empirical Evaluation of Ten Depth Cameras: Bias, Precision, Lateral Noise, Different Lighting Conditions and Materials, and Multiple Sensor Setups in Indoor Environments","volume":"26","author":"Suchi","year":"2019","journal-title":"IEEE Robot. Autom. Mag."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1356\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:24:11Z","timestamp":1760160251000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1356"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,14]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21041356"],"URL":"https:\/\/doi.org\/10.3390\/s21041356","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,2,14]]}}}