{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T15:03:47Z","timestamp":1750950227772},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T00:00:00Z","timestamp":1649289600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T00:00:00Z","timestamp":1649289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100009087","name":"Universidad Industrial de Santander","doi-asserted-by":"publisher","award":["2697"],"award-info":[{"award-number":["2697"]}],"id":[{"id":"10.13039\/501100009087","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s11042-022-12280-w","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T08:03:47Z","timestamp":1649318627000},"page":"30733-30748","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A local volumetric covariance descriptor for markerless Parkinsonian gait pattern quantification"],"prefix":"10.1007","volume":"81","author":[{"given":"Oscar","family":"Mendoza","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabio","family":"Mart\u00ednez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Olmos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,7]]},"reference":[{"key":"12280_CR1","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.future.2018.02.009","volume":"83","author":"E Abdulhay","year":"2018","unstructured":"Abdulhay E., Arunkumar N., Narasimhan K., Vellaiappan E., Venkatraman V. (2018) Gait and tremor investigation using machine learning techniques for the diagnosis of parkinson disease. Futur Gener Comput Syst 83:366\u2013373","journal-title":"Futur Gener Comput Syst"},{"key":"12280_CR2","doi-asserted-by":"publisher","first-page":"105442","DOI":"10.1016\/j.clineuro.2019.105442","volume":"184","author":"M Beli\u0107","year":"2019","unstructured":"Beli\u0107 M., Bobi\u0107 V., Bad\u017ea M., \u0160,olaja N., \u00d0uri\u0107-Jovi\u010di\u0107 M., Kosti\u0107 V.S. (2019) Artificial intelligence for assisting diagnostics and assessment of parkinson\u2019s disease\u2014a review. Clin Neurol Neurosurg 184:105442","journal-title":"Clin Neurol Neurosurg"},{"issue":"3","key":"12280_CR3","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1002\/pri.1579","volume":"19","author":"S Bovonsunthonchai","year":"2014","unstructured":"Bovonsunthonchai S., Vachalathiti R., Pisarnpong A., Khobhun F., Hiengkaew V. (2014) Spatiotemporal gait parameters for patients with parkinson\u2019s disease compared with normal individuals. Physiother Res Int 19(3):158\u2013165","journal-title":"Physiother Res Int"},{"issue":"1","key":"12280_CR4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"issue":"6","key":"12280_CR5","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.1109\/JBHI.2018.2865218","volume":"22","author":"C Caramia","year":"2018","unstructured":"Caramia C., Torricelli D., Schmid M., Mu\u00f1oz-Gonzalez A., Gonzalez-Vargas J., Grandas F., Pons J. L. (2018) Imu-based classification of parkinson\u2019s disease from gait: a sensitivity analysis on sensor location and feature selection. IEEE J Biomed Health Inform 22(6):1765\u20131774","journal-title":"IEEE J Biomed Health Inform"},{"issue":"3","key":"12280_CR6","doi-asserted-by":"publisher","first-page":"e87640","DOI":"10.1371\/journal.pone.0087640","volume":"9","author":"E Ceseracciu","year":"2014","unstructured":"Ceseracciu E., Sawacha Z., Cobelli C. (2014) Comparison of markerless and marker-based motion capture technologies through simultaneous data collection during gait: proof of concept. PloS one 9(3):e87640","journal-title":"PloS one"},{"issue":"1","key":"12280_CR7","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1177\/1545968316656057","volume":"31","author":"MH Cole","year":"2017","unstructured":"Cole M. H., Naughton G. A., Silburn P. A. (2017) Neuromuscular impairments are associated with impaired head and trunk stability during gait in parkinson fallers. Neurorehab Neural Re 31(1):34\u201347","journal-title":"Neurorehab Neural Re"},{"key":"12280_CR8","doi-asserted-by":"crossref","unstructured":"Deng J., Dong W., Socher R., Li L. J., Li K., Fei-Fei L. (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on computer vision and pattern recognition, pp. 248\u2013255. IEEE","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"1","key":"12280_CR9","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1001\/jamaneurol.2017.3299","volume":"75","author":"ER Dorsey","year":"2018","unstructured":"Dorsey E. R., Bloem B. R. (2018) The parkinson pandemic\u2014a call to action. JAMA neurology 75(1):9\u201310","journal-title":"JAMA neurology"},{"issue":"3","key":"12280_CR10","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.gaitpost.2015.06.007","volume":"42","author":"RP Duncan","year":"2015","unstructured":"Duncan R. P., Leddy A. L., Cavanaugh J. T., Dibble L. E., Ellis T. D., Ford M. P., Foreman K. B., Earhart G. M. (2015) Balance differences in people with parkinson disease with and without freezing of gait. Gait & posture 42 (3):306\u2013309","journal-title":"Gait & posture"},{"key":"12280_CR11","doi-asserted-by":"crossref","unstructured":"Farneb\u00e4ck G. (2003) Two-frame motion estimation based on polynomial expansion. In: Scandinavian conference on image analysis, pp. 363\u2013370. Springer","DOI":"10.1007\/3-540-45103-X_50"},{"key":"12280_CR12","doi-asserted-by":"crossref","unstructured":"Guayac\u00e1n L.C., Mart\u00ednez F. (2021) Visualising and quantifying relevant parkinsonian gait patterns using 3d convolutional network, vol 123","DOI":"10.1016\/j.jbi.2021.103935"},{"issue":"1","key":"12280_CR13","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/S0966-6362(98)00020-4","volume":"8","author":"SE Halliday","year":"1998","unstructured":"Halliday S. E., Winter D. A., Frank J. S., Patla A. E., Prince F. (1998) The initiation of gait in young, elderly, and parkinson\u2019s disease subjects. Gait & posture 8(1):8\u201314","journal-title":"Gait & posture"},{"key":"12280_CR14","doi-asserted-by":"crossref","unstructured":"Huang G., Liu Z., Van Der Maaten L., Weinberger K. Q. (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"12280_CR15","unstructured":"Hussein M. E., Torki M., Gowayyed M. A., El-Saban M. (2013) Human action recognition using a temporal hierarchy of covariance descriptors on 3d joint locations. In: Twenty-third international joint conference on artificial intelligence"},{"key":"12280_CR16","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.compbiomed.2019.03.025","volume":"108","author":"AJ Larrazabal","year":"2019","unstructured":"Larrazabal A. J., Cena C. G., Mart\u00ednez C.E. (2019) Video-oculography eye tracking towards clinical applications: A review. Comput Biol Med 108:57\u201366","journal-title":"Comput Biol Med"},{"issue":"6","key":"12280_CR17","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1093\/gerona\/glp009","volume":"64","author":"MD Latt","year":"2009","unstructured":"Latt M. D., Menz H. B., Fung V. S., Lord S. R. (2009) Acceleration patterns of the head and pelvis during gait in older people with parkinson\u2019s disease: a comparison of fallers and nonfallers. J Gerontol A Biol Sci Med Sci 64(6):700\u2013706","journal-title":"J Gerontol A Biol Sci Med Sci"},{"key":"12280_CR18","unstructured":"Lucas B. D., Kanade T., et al. (1981) an iterative image registration technique with an application to stereo vision"},{"issue":"6-7","key":"12280_CR19","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.imavis.2014.04.002","volume":"32","author":"B Ma","year":"2014","unstructured":"Ma B., Su Y., Jurie F. (2014) Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image Vis Comput 32 (6-7):379\u2013390","journal-title":"Image Vis Comput"},{"key":"12280_CR20","doi-asserted-by":"crossref","unstructured":"Matikainen P., Hebert M., Sukthankar R. (2009) Trajectons: Action recognition through the motion analysis of tracked features. In: 2009 IEEE 12Th international conference on computer vision workshops, ICCV workshops, pp. 514\u2013521. IEEE","DOI":"10.1109\/ICCVW.2009.5457659"},{"key":"12280_CR21","doi-asserted-by":"crossref","unstructured":"Messing R., Pal C., Kautz H. (2009) Activity recognition using the velocity histories of tracked keypoints. In: 2009 IEEE 12Th international conference on computer vision, pp. 104\u2013111. IEEE","DOI":"10.1109\/ICCV.2009.5459154"},{"key":"12280_CR22","doi-asserted-by":"crossref","unstructured":"Minh H. Q., Murino V. (2017) Covariances in computer vision and machine learning morgan & claypool publishers","DOI":"10.1007\/978-3-031-01820-6"},{"issue":"4","key":"12280_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-01820-6","volume":"7","author":"HQ Minh","year":"2017","unstructured":"Minh H. Q., Murino V. (2017) Covariances in computer vision and machine learning. Synthesis Lectures on Computer Vision 7(4):1\u2013170","journal-title":"Synthesis Lectures on Computer Vision"},{"issue":"5","key":"12280_CR24","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1109\/TNSRE.2019.2910165","volume":"27","author":"N Naghavi","year":"2019","unstructured":"Naghavi N., Wade E. (2019) Prediction of freezing of gait in parkinson\u2019s disease using statistical inference and lower\u2013limb acceleration data. IEEE Trans Neural Syst Rehabilitation Eng 27(5):947\u2013955","journal-title":"IEEE Trans Neural Syst Rehabilitation Eng"},{"issue":"1","key":"12280_CR25","doi-asserted-by":"publisher","first-page":"28","DOI":"10.11138\/FNeur\/2017.32.1.028","volume":"32","author":"M Pistacchi","year":"2017","unstructured":"Pistacchi M., Gioulis M., Sanson F., De Giovannini E., Filippi G., Rossetto F., Marsala S. Z. (2017) Gait analysis and clinical correlations in early parkinson\u2019s disease. Funct Neurol 32(1):28","journal-title":"Funct Neurol"},{"issue":"1","key":"12280_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/nrdp.2017.13","volume":"3","author":"W Poewe","year":"2017","unstructured":"Poewe W., Seppi K., Tanner C. M., Halliday G. M., Brundin P., Volkmann J., Schrag A. E., Lang A. E. (2017) Parkinson disease. Nat Rev Dis Primers 3(1):1\u201321","journal-title":"Nat Rev Dis Primers"},{"issue":"6","key":"12280_CR27","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1212\/WNL.0000000000002350","volume":"86","author":"G Rizzo","year":"2016","unstructured":"Rizzo G., Copetti M., Arcuti S., Martino D., Fontana A., Logroscino G. (2016) Accuracy of clinical diagnosis of parkinson disease: a systematic review and meta-analysis. Neurology 86(6):566\u2013576","journal-title":"Neurology"},{"key":"12280_CR28","doi-asserted-by":"crossref","unstructured":"Sandler M., Howard A., Zhu M., Zhmoginov A., Chen L. C. (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"12280_CR29","unstructured":"Sun J., Wu X., Yan S., Cheong L. F., Chua T. S., Li J. (2009) Hierarchical spatio-temporal context modeling for action recognition. In: 2009 IEEE Conference on computer vision and pattern recognition, pp. 2004\u20132011. IEEE"},{"issue":"2","key":"12280_CR30","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/S0966-6362(02)00004-8","volume":"16","author":"DH Sutherland","year":"2002","unstructured":"Sutherland D. H. (2002) The evolution of clinical gait analysis: Part ii kinematics. Gait & posture 16(2):159\u2013179","journal-title":"Gait & posture"},{"key":"12280_CR31","doi-asserted-by":"crossref","unstructured":"Thenganatt M. A., Jankovic J. (2014) Psychogenic tremor: a video guide to its distinguishing features. Tremor Other Hyperkinet Mov:4","DOI":"10.5334\/tohm.228"},{"key":"12280_CR32","doi-asserted-by":"crossref","unstructured":"Tuzel O., Porikli F., Meer P. (2006) Region covariance: a fast descriptor for detection and classification. In: European conference on computer vision, pp. 589\u2013600. Springer","DOI":"10.1007\/11744047_45"},{"key":"12280_CR33","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1109\/ACCESS.2017.2778011","volume":"6","author":"A Ullah","year":"2017","unstructured":"Ullah A., Ahmad J., Muhammad K., Sajjad M., Baik S. W. (2017) Action recognition in video sequences using deep bi-directional lstm with cnn features. IEEE Access 6:1155\u20131166","journal-title":"IEEE Access"},{"issue":"6","key":"12280_CR34","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1109\/TPAMI.2017.2712608","volume":"40","author":"G Varol","year":"2017","unstructured":"Varol G., Laptev I., Schmid C. (2017) Long-term temporal convolutions for action recognition. IEEE Trans Pattern Anal Mach Intell 40(6):1510\u20131517","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"9","key":"12280_CR35","doi-asserted-by":"publisher","first-page":"2743","DOI":"10.3390\/s18092743","volume":"18","author":"TT Verlekar","year":"2018","unstructured":"Verlekar T. T., Soares L. D., Correia P. L. (2018) Automatic classification of gait impairments using a markerless 2d video-based system. Sensors 18 (9):2743","journal-title":"Sensors"},{"key":"12280_CR36","doi-asserted-by":"crossref","unstructured":"Verlekar T. T., Soares L. D., Correia P. L. (2018) Automatic classification of gait impairments using a markerless 2d video-based system Sensors","DOI":"10.3390\/s18092743"},{"key":"12280_CR37","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1016\/S0140-6736(17)32154-2","volume":"390","author":"T Vos","year":"2016","unstructured":"Vos T., et al. (2016) Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990\u20132016: a systematic analysis for the global burden of disease study 2016. The Lancet 390:1211\u20131259","journal-title":"The Lancet"},{"issue":"1","key":"12280_CR38","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/s11263-012-0594-8","volume":"103","author":"H Wang","year":"2013","unstructured":"Wang H., Kl\u00e4ser A., Schmid C., Liu C. L. (2013) Dense trajectories and motion boundary descriptors for action recognition. Int J Comput Vis 103(1):60\u201379","journal-title":"Int J Comput Vis"},{"key":"12280_CR39","doi-asserted-by":"crossref","unstructured":"Wang H., Schmid C. (2013) Action recognition with improved trajectories. In: Proceedings of the IEEE international conference on computer vision, pp. 3551\u20133558","DOI":"10.1109\/ICCV.2013.441"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12280-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-12280-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12280-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T05:37:37Z","timestamp":1660714657000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-12280-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,7]]},"references-count":39,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["12280"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-12280-w","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,7]]},"assertion":[{"value":"10 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}