{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:40:09Z","timestamp":1775839209228,"version":"3.50.1"},"reference-count":19,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T00:00:00Z","timestamp":1619740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovate UK","award":["102719"],"award-info":[{"award-number":["102719"]}]},{"name":"MIGHT (Malaysia Industry-Government for High Technology)","award":["62873-455315"],"award-info":[{"award-number":["62873-455315"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>In this article, we present a dataset that comprises different physical rehabilitation movements. The dataset was captured as part of a research project intended to provide automatic feedback on the execution of rehabilitation exercises, even in the absence of a physiotherapist. A Kinect motion sensor camera was used to record gestures. The dataset contains repetitions of nine gestures performed by 29 subjects, out of which 15 were patients and 14 were healthy controls. The data are presented in an easily accessible format, provided as 3D coordinates of 25 body joints along with the corresponding depth map for each frame. Each movement was annotated with the gesture type, the position of the person performing the gesture (sitting or standing) as well as a correctness label. The data are publicly available and were released with to provide a comprehensive dataset that can be used for assessing the performance of different patients while performing simple movements in a rehabilitation setting and for comparing these movements with a control group of healthy individuals.<\/jats:p>","DOI":"10.3390\/data6050046","type":"journal-article","created":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T05:10:55Z","timestamp":1619759455000},"page":"46","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["IntelliRehabDS (IRDS)\u2014A Dataset of Physical Rehabilitation Movements"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0068-4495","authenticated-orcid":false,"given":"Alina","family":"Miron","sequence":"first","affiliation":[{"name":"Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2195-8264","authenticated-orcid":false,"given":"Noureddin","family":"Sadawi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK"},{"name":"Biotechnology Research Center, Tripoli TIP3644, Libya"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4544-636X","authenticated-orcid":false,"given":"Waidah","family":"Ismail","sequence":"additional","affiliation":[{"name":"International Halal and Fatwa Center, Universiti Sains Islam, Nilai 71800, Malaysia"},{"name":"Faculty of Science and Technology, Universiti Sains Islam, Nilai 71800, Malaysia"},{"name":"Information System Study Program, Universitas Airlangga, Indonesia Kampus C, Surabaya, Jawa Timur 60115, Indonesia"}]},{"given":"Hafez","family":"Hussain","sequence":"additional","affiliation":[{"name":"Perkeso Rehabilitation Centre, Bemban, Melaka 75450, Malaysia"}]},{"given":"Crina","family":"Grosan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,30]]},"reference":[{"key":"ref_1","unstructured":"(2021, April 29). Kinect\u2014Windows App Development. Available online: https:\/\/developer.microsoft.com\/en-us\/windows\/kinect."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kitsunezaki, N., Adachi, E., Masuda, T., and Mizusawa, J. (2013, January 4\u20135). KINECT applications for the physical rehabilitation. Proceedings of the 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Gatineau, QC, Canada.","DOI":"10.1109\/MeMeA.2013.6549755"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"52532","DOI":"10.1109\/ACCESS.2019.2911705","article-title":"Accurate Hierarchical Human Actions Recognition From Kinect Skeleton Data","volume":"7","author":"Su","year":"2019","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chiuchisan, I., Geman, O., and Postolache, O. (2018, January 18\u201319). Future trends in exergaming using MS Kinect for medical rehabilitation. Proceedings of the 2018 International Conference and Exposition on Electrical And Power Engineering (EPE), Iasi, Romania.","DOI":"10.1109\/ICEPE.2018.8559924"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Vakanski, A., Jun, H.P., Paul, D., and Baker, R. (2018). A data set of human body movements for physical rehabilitation exercises. Data, 3.","DOI":"10.3390\/data3010002"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Paiement, A., Tao, L., Hannuna, S., Camplani, M., Damen, D., and Mirmehdi, M. (2014, January 1\u20135). Online quality assessment of human movement from skeleton data. Proceedings of the British Machine Vision Conference, Nottingham, UK.","DOI":"10.5244\/C.28.79"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Parisi, G.I., von Stosch, F., Magg, S., and Wermter, S. (2015, January 12\u201316). Learning human motion feedback with neural self-organization. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland.","DOI":"10.1109\/IJCNN.2015.7280701"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Devineau, G., Moutarde, F., Xi, W., and Yang, J. (2018, January 15\u201319). Deep learning for hand gesture recognition on skeletal data. Proceedings of the 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00025"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3132369","article-title":"Analysis of movement quality in full-body physical activities","volume":"9","author":"Niewiadomski","year":"2019","journal-title":"ACM Trans. Interact. Intell. Syst. TiiS"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s10462-018-9623-5","article-title":"A performance comparison of machine learning classification approaches for robust activity of daily living recognition","volume":"52","author":"Hussain","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ebert, A., Beck, M.T., Mattausch, A., Belzner, L., and Linnhoff-Popien, C. (September, January 28). Qualitative assessment of recurrent human motion. Proceedings of the 2017 25th European Signal Processing Conference (EUSIPCO), Kos Island, Greece.","DOI":"10.23919\/EUSIPCO.2017.8081218"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dolatabadi, E., Zhi, Y.X., Ye, B., Coahran, M., Lupinacci, G., Mihailidis, A., and Taati, B. (2017, January 23\u201326). The toronto rehab stroke pose dataset to detect compensation during stroke rehabilitation therapy. Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, Barcelona, Spain.","DOI":"10.1145\/3154862.3154925"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1555008","DOI":"10.1142\/S0218001415550083","article-title":"A survey of applications and human motion recognition with microsoft kinect","volume":"29","author":"Lun","year":"2015","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1109\/TNSRE.2019.2923060","article-title":"The KIMORE Dataset: KInematic Assessment of MOvement and Clinical Scores for Remote Monitoring of Physical REhabilitation","volume":"27","author":"Capecci","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_15","first-page":"428","article-title":"Generative Adversarial Networks for Generation and Classification of Physical Rehabilitation Movement Episodes","volume":"8","author":"Li","year":"2018","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Um, T.T., Pfister, F.M., Pichler, D., Endo, S., Lang, M., Hirche, S., and Kuli\u0107, D. (2017, January 13\u201317). Data Augmentation of Wearable Sensor Data for Parkinson\u2019s Disease Monitoring Using Convolutional Neural Networks. Proceedings of the 19th ACM International Conference on Multimodal Interaction, Glasgow, UK.","DOI":"10.1145\/3136755.3136817"},{"key":"ref_17","unstructured":"(2021, April 22). Intel\u00ae RealSense\u2122 Depth and Tracking Cameras. Available online: https:\/\/www.intelrealsense.com\/depth-camera-d435\/."},{"key":"ref_18","unstructured":"(2021, April 22). Orbec Astra. Available online: https:\/\/orbbec3d.com."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proc. IEEE"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/6\/5\/46\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:55:56Z","timestamp":1760162156000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/6\/5\/46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,30]]},"references-count":19,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["data6050046"],"URL":"https:\/\/doi.org\/10.3390\/data6050046","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,30]]}}}