{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T10:03:39Z","timestamp":1780481019308,"version":"3.54.1"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032067739","type":"print"},{"value":"9783032067746","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-06774-6_3","type":"book-chapter","created":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T07:58:22Z","timestamp":1759564702000},"page":"29-43","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Unified Pipeline for\u00a0Explainable Gait Analysis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3187-0428","authenticated-orcid":false,"given":"Daniel","family":"Zieger","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4413-3708","authenticated-orcid":false,"given":"Jann-Ole","family":"Henningson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4736-2397","authenticated-orcid":false,"given":"Bernhard","family":"Egger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8699-3442","authenticated-orcid":false,"given":"Marc","family":"Stamminger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,5]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","unstructured":"Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138\u201352160 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2870052","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"3_CR2","doi-asserted-by":"publisher","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLOS ONE 10(7), 1\u201346 (2015). https:\/\/doi.org\/10.1371\/journal.pone.0130140","DOI":"10.1371\/journal.pone.0130140"},{"key":"3_CR3","first-page":"1803","volume":"11","author":"D Baehrens","year":"2010","unstructured":"Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., M\u00fcller, K.R.: How to explain individual classification decisions. J. Mach. Learn. Res. 11, 1803\u20131831 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Bashirov, R., et al.: Real-time RGBD-based extended body pose estimation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2807\u20132816 (2021)","DOI":"10.1109\/WACV48630.2021.00285"},{"issue":"2","key":"3_CR5","first-page":"353","volume":"9","author":"M Burnfield","year":"2010","unstructured":"Burnfield, M.: Gait analysis: normal and pathological function. J. Sports Sci. Med. 9(2), 353 (2010)","journal-title":"J. Sports Sci. Med."},{"key":"3_CR6","doi-asserted-by":"publisher","unstructured":"Cho, C.W., Chao, W.H., Lin, S.H., Chen, Y.Y.: A vision-based analysis system for gait recognition in patients with Parkinson\u2019s disease. Expert Syst. Appl. 36(3, Part 2), 7033\u20137039 (2009). https:\/\/doi.org\/10.1016\/j.eswa.2008.08.076, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417408006003","DOI":"10.1016\/j.eswa.2008.08.076"},{"issue":"7","key":"3_CR7","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1016\/j.measurement.2012.04.013","volume":"45","author":"MR Daliri","year":"2012","unstructured":"Daliri, M.R.: Automatic diagnosis of neuro-degenerative diseases using gait dynamics. Measurement 45(7), 1729\u20131734 (2012)","journal-title":"Measurement"},{"key":"3_CR8","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. CoRR abs\/2010.11929 (2020). https:\/\/arxiv.org\/abs\/2010.11929"},{"key":"3_CR9","unstructured":"G\u00f6tz-Neumann, K.: Gehen verstehen: Ganganalyse in der physiotherapie; 18 Tabellen. Georg Thieme Verlag (2006)"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"He, T., Xu, Y., Saito, S., Soatto, S., Tung, T.: Arch++: animation-ready clothed human reconstruction revisited. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2021). http:\/\/arxiv.org\/abs\/2108.07845v1","DOI":"10.1109\/ICCV48922.2021.01086"},{"key":"3_CR11","doi-asserted-by":"publisher","unstructured":"Jakob, V., et al.: Validation of a sensor-based gait analysis system with a gold-standard motion capture system in patients with Parkinson\u2019s disease. Sensors 21(22) (2021). https:\/\/doi.org\/10.3390\/s21227680, https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7680","DOI":"10.3390\/s21227680"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Kanko, R.M., et al.: Assessment of spatiotemporal gait parameters using a deep learning algorithm-based Markerless motion capture system. J. Biomech. 122, 110414 (2021)","DOI":"10.1016\/j.jbiomech.2021.110414"},{"key":"3_CR13","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Published as a Conference Paper at the 3rd International Conference for Learning Representations, San Diego (2015). https:\/\/arxiv.org\/abs\/1412.6980 (2014)"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Kocabas, M., Athanasiou, N., Black, M.J.: Vibe: Video inference for human body pose and shape estimation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00530"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Lawton, M.P.: Quality of life in Alzheimer disease. Alzheimer Dis. Assoc. Disord. 8(3), 138\u2013150 (1994)","DOI":"10.1097\/00002093-199404000-00015"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Lee, S.H., Lim, J.S.: Parkinson\u2019s disease classification using gait characteristics and wavelet-based feature extraction. Expert Syst. Appl. 39(8), 7338\u20137344 (2012)","DOI":"10.1016\/j.eswa.2012.01.084"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Letham, B., Rudin, C., McCormick, T.H., Madigan, D.: Interpretable classifiers using rules and Bayesian analysis: building a better stroke prediction model. Ann. Appl. Stat. 9(3), 1350\u20131371 (2015)","DOI":"10.1214\/15-AOAS848"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Li, T., Bolkart, T., Black, M.J., Li, H., Romero, J.: Learning a model of facial shape and expression from 4D scans. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 36(6), 194:1\u2013194:17 (2017). https:\/\/doi.org\/10.1145\/3130800.3130813","DOI":"10.1145\/3130800.3130813"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graphics (Proc. SIGGRAPH Asia) 34(6), 248:1\u2013248:16 (2015)","DOI":"10.1145\/2816795.2818013"},{"issue":"1","key":"3_CR20","first-page":"157","volume":"58","author":"R Martinec","year":"2019","unstructured":"Martinec, R., Pinjatela, R., Balen, D.: Quality of life in patients with rheumatoid arthritis - a preliminary study. Acta Clin. Croat. 58(1), 157\u2013166 (2019)","journal-title":"Acta Clin. Croat."},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Mirelman, A., et al.: Gait impairments in Parkinson\u2019s disease. Lancet Neurol. 18(7), 697\u2013708 (2019)","DOI":"10.1016\/S1474-4422(19)30044-4"},{"issue":"5","key":"3_CR22","doi-asserted-by":"publisher","first-page":"1987","DOI":"10.1002\/jcsm.13312","volume":"14","author":"KT Murphy","year":"2023","unstructured":"Murphy, K.T., Lynch, G.S.: Impaired skeletal muscle health in parkinsonian syndromes: clinical implications, mechanisms and potential treatments. J. Cachexia. Sarcopenia Muscle 14(5), 1987\u20132002 (2023)","journal-title":"J. Cachexia. Sarcopenia Muscle"},{"key":"3_CR23","unstructured":"Nguyen, A., Dosovitskiy, A., Yosinski, J., Brox, T., Clune, J.: Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 3395\u20133403. NIPS 2016, Curran Associates Inc., Red Hook, NY, USA (2016)"},{"key":"3_CR24","doi-asserted-by":"publisher","unstructured":"Osman, A.A.A., Bolkart, T., Tzionas, D., Black, M.J.: SUPR: a sparse unified part-based human body model. In: European Conference on Computer Vision (ECCV) (2022). https:\/\/doi.org\/10.1007\/978-3-031-20086-1_33, https:\/\/supr.is.tue.mpg.de","DOI":"10.1007\/978-3-031-20086-1_33"},{"key":"3_CR25","doi-asserted-by":"publisher","unstructured":"Park, H., Shin, S., Youm, C., Cheon, S.M., Lee, M., Noh, B.: Classification of Parkinson\u2019s disease with freezing of gait based on 360$$^\\circ $$ turning analysis using 36 kinematic features. J. Neuroeng. Rehabil. 18(1), 177 (2021). https:\/\/doi.org\/10.1186\/s12984-021-00975-4","DOI":"10.1186\/s12984-021-00975-4"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Pau, M., et al.: Quantitative assessment of gait parameters in people with Parkinson\u2019s disease in laboratory and clinical setting: are the measures interchangeable? Neurol. Int. 10(2), 7729 (2018)","DOI":"10.4081\/ni.2018.7729"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., et al.: Expressive body capture: 3D hands, face, and body from a single image. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.01123"},{"key":"3_CR28","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cwhy should I trust you?\u201d: explaining the predictions of any classifier. CoRR abs\/1602.04938 (2016). http:\/\/arxiv.org\/abs\/1602.04938"},{"key":"3_CR29","first-page":"380","volume":"5","author":"O Rodrigues","year":"1840","unstructured":"Rodrigues, O.: Des lois g\u00e9om\u00e9triques qui r\u00e9gissent les d\u00e9placements d\u2019un syst\u00e8me solide dans l\u2019espace, et de la variation des coordonn\u00e9es provenant de ces d\u00e9placements consid\u00e9r\u00e9s ind\u00e9pendamment des causes qui peuvent les produire. J. de math\u00e9matiques pures et appliqu\u00e9es 5, 380\u2013440 (1840)","journal-title":"J. de math\u00e9matiques pures et appliqu\u00e9es"},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Romero, J., Tzionas, D., Black, M.J.: Embodied hands: modeling and capturing hands and bodies together. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 36(6), 245:1\u2013245:17 (2017). http:\/\/doi.acm.org\/10.1145\/3130800.3130883","DOI":"10.1145\/3130800.3130883"},{"key":"3_CR31","doi-asserted-by":"crossref","unstructured":"Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., Li, H.: PIFU: pixel-aligned implicit function for high-resolution clothed human digitization. In: The IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00239"},{"key":"3_CR32","doi-asserted-by":"publisher","unstructured":"Schettino, R.B.: Rodrigobdz\/LRP: v0.1.6 (2022). https:\/\/doi.org\/10.5281\/zenodo.6821295","DOI":"10.5281\/zenodo.6821295"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Schleicher, R., et al.: BASH: biomechanical animated skinned human for visualization of kinematics and muscle activity. In: Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SCITEPRESS - Science and Technology Publications (2021)","DOI":"10.5220\/0010210600250036"},{"key":"3_CR34","doi-asserted-by":"publisher","unstructured":"Slijepcevic, D., et al.: Explaining machine learning models for clinical gait analysis. ACM Trans. Comput. Healthcare 3(2) (2021). https:\/\/doi.org\/10.1145\/3474121","DOI":"10.1145\/3474121"},{"issue":"4","key":"3_CR35","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/S1474-4422(24)00038-3","volume":"23","author":"JD Steinmetz","year":"2024","unstructured":"Steinmetz, J.D., et al.: Global, regional, and national burden of disorders affecting the nervous system, 1990\u20132021: a systematic analysis for the global burden of disease study 2021. Lancet Neurol. 23(4), 344\u2013381 (2024). https:\/\/doi.org\/10.1016\/S1474-4422(24)00038-3","journal-title":"Lancet Neurol."},{"key":"3_CR36","unstructured":"Taniguchi, S., et al.: Adopt: Modified Adam can converge with any 2 with the optimal rate. In: Advances in Neural Information Processing Systems (2024)"},{"key":"3_CR37","doi-asserted-by":"crossref","unstructured":"Tretschk, E., et al.: State of the art in dense monocular non-rigid 3D reconstruction. Comput. Graph. Forum 42(2), 485\u2013520 (2023)","DOI":"10.1111\/cgf.14774"},{"key":"3_CR38","doi-asserted-by":"crossref","unstructured":"Weng, C.Y., Curless, B., Srinivasan, P.P., Barron, J.T., Kemelmacher-Shlizerman, I.: HumanNeRF: free-viewpoint rendering of moving people from monocular video. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16210\u201316220 (2022)","DOI":"10.1109\/CVPR52688.2022.01573"},{"key":"3_CR39","doi-asserted-by":"crossref","unstructured":"Wirth, V., et al.: Sharpy: shape reconstruction and hand pose estimation from RGB-D with uncertainty. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 2625\u20132633 (2023)","DOI":"10.1109\/ICCVW60793.2023.00277"},{"key":"3_CR40","doi-asserted-by":"crossref","unstructured":"Yang, C., Rangarajan, A., Ranka, S.: Global model interpretation via recursive partitioning. In: 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS). IEEE (2018)","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2018.00256"},{"key":"3_CR41","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1016\/j.neulet.2016.09.043","volume":"633","author":"W Zeng","year":"2016","unstructured":"Zeng, W., Liu, F., Wang, Q., Wang, Y., Ma, L., Zhang, Y.: Parkinson\u2019s disease classification using gait analysis via deterministic learning. Neurosci. Lett. 633, 268\u2013278 (2016)","journal-title":"Neurosci. Lett."},{"key":"3_CR42","doi-asserted-by":"crossref","unstructured":"Zhao, N., et al.: Quality of life in Parkinson\u2019s disease: a systematic review and meta-analysis of comparative studies. CNS Neurosci. Ther. 27(3), 270\u2013279 (2021)","DOI":"10.1111\/cns.13549"},{"issue":"6","key":"3_CR43","doi-asserted-by":"publisher","first-page":"3170","DOI":"10.1109\/TPAMI.2021.3050505","volume":"44","author":"Z Zheng","year":"2022","unstructured":"Zheng, Z., Yu, T., Liu, Y., Dai, Q.: Pamir: parametric model-conditioned implicit representation for image-based human reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 3170\u20133184 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3050505","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR44","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Yu, T., Wei, Y., Dai, Q., Liu, Y.: DeepHuman: 3D human reconstruction from a single image. In: The IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00783"},{"key":"3_CR45","doi-asserted-by":"publisher","unstructured":"Zieger, D., et al.: 3D body Twin: improving human gait visualizations using personalized avatars. In: ShapeMI 2024, pp. 70\u201383. ShapeMI: International Workshop on Shape in Medical Imaging (2024). https:\/\/doi.org\/10.1007\/978-3-031-75291-9_6","DOI":"10.1007\/978-3-031-75291-9_6"}],"container-title":["Lecture Notes in Computer Science","Shape in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-06774-6_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T09:06:00Z","timestamp":1780477560000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-06774-6_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,5]]},"ISBN":["9783032067739","9783032067746"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-06774-6_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,5]]},"assertion":[{"value":"5 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ShapeMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Shape in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"shapemi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/shapemi.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}