{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T09:56:16Z","timestamp":1743069376534,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030898199"},{"type":"electronic","value":"9783030898205"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-89820-5_19","type":"book-chapter","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T20:35:26Z","timestamp":1634762126000},"page":"228-239","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Framework for\u00a0Antalgic Gait Recognition Based on\u00a0Human Activity"],"prefix":"10.1007","author":[{"given":"Juan-Carlos","family":"Gonzalez-Islas","sequence":"first","affiliation":[]},{"given":"Omar-Arturo","family":"Dominguez-Ramirez","sequence":"additional","affiliation":[]},{"given":"Omar","family":"Lopez-Ortega","sequence":"additional","affiliation":[]},{"given":"Rene-Daniel","family":"Paredes-Bautista","sequence":"additional","affiliation":[]},{"given":"David","family":"Diazgiron-Aguilar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"19_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1007\/978-3-642-35395-6_30","volume-title":"Ambient Assisted Living and Home Care","author":"D Anguita","year":"2012","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: Bravo, J., Herv\u00e1s, R., Rodr\u00edguez, M. (eds.) IWAAL 2012. LNCS, vol. 7657, pp. 216\u2013223. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-35395-6_30"},{"key":"19_CR2","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.compmedimag.2019.01.007","volume":"73","author":"A Brahim","year":"2019","unstructured":"Brahim, A., et al.: A decision support tool for early detection of knee osteoarthritis using x-ray imaging and machine learning: data from the osteoarthritis initiative. Comput. Med. Imaging Graph. 73, 11\u201318 (2019)","journal-title":"Comput. Med. Imaging Graph."},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Brenton-Rule, A., Mattock, J., Carroll, M., et al.: Reliability of the tekscan matscan\u00ae system for the measurement of postural stability in older people with rheumatoid arthritis. J. Foot Ankle Res. 5(1), 21 (2012)","DOI":"10.1186\/1757-1146-5-21"},{"key":"19_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2018.01.007","volume":"167","author":"P Connor","year":"2018","unstructured":"Connor, P., Ross, A.: Biometric recognition by gait: a survey of modalities and features. Comput. Vis. Image Underst. 167, 1\u201327 (2018)","journal-title":"Comput. Vis. Image Underst."},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Derawi, M., Bours, P.: Gait and activity recognition using commercial phones. Comput. Secur. 39, 137\u2013144 (2013)","DOI":"10.1016\/j.cose.2013.07.004"},{"key":"19_CR6","first-page":"S112","volume":"27","author":"SSS Fathima","year":"2016","unstructured":"Fathima, S.S.S., Banu, W.R.: Abnormal walk identification for systems using gait patterns. Biomed. Res. India 27, S112\u2013S117 (2016)","journal-title":"Biomed. Res. India"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Frigui, H.: Clustering: algorithms and applications. In: 2008 First Workshops on Image Processing Theory, Tools and Applications, pp. 1\u201311. IEEE (2008)","DOI":"10.1109\/IPTA.2008.4743793"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Gafurov, D., Helkala, K., S\u00f8ndrol, T.: Gait recognition using acceleration from mems. In: First International Conference on Availability, Reliability and Security (ARES 2006), p. 6. IEEE (2006)","DOI":"10.1109\/ARES.2006.68"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Gafurov, D., Snekkenes, E., Bours, P.: Gait authentication and identification using wearable accelerometer sensor. In: 2007 IEEE Workshop on Automatic Identification Advanced Technologies, pp. 220\u2013225. IEEE (2007)","DOI":"10.1109\/AUTOID.2007.380623"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Gu, X., Deligianni, F., Lo, B., Chen, W., Yang, G.Z.: Markerless gait analysis based on a single RGB camera. In: 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 42\u201345. IEEE (2018)","DOI":"10.1109\/BSN.2018.8329654"},{"issue":"2","key":"19_CR11","doi-asserted-by":"publisher","first-page":"333","DOI":"10.3745\/JIPS.2013.9.2.333","volume":"9","author":"T Hoang","year":"2013","unstructured":"Hoang, T., Nguyen, T., Luong, C., Do, S., Choi, D.: Adaptive cross-device gait recognition using a mobile accelerometer. J. Inf. Process. Syst. 9(2), 333\u2013348 (2013)","journal-title":"J. Inf. Process. Syst."},{"key":"19_CR12","doi-asserted-by":"publisher","first-page":"139881","DOI":"10.1109\/ACCESS.2020.3013029","volume":"8","author":"K Jun","year":"2020","unstructured":"Jun, K., Lee, Y., Lee, S., Lee, D.W., Kim, M.S.: Pathological gait classification using kinect v2 and gated recurrent neural networks. IEEE Access 8, 139881\u2013139891 (2020)","journal-title":"IEEE Access"},{"issue":"8","key":"19_CR13","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1080\/03091902.2020.1822940","volume":"44","author":"P Khera","year":"2020","unstructured":"Khera, P., Kumar, N.: Role of machine learning in gait analysis: a review. J. Med. Eng. Technol. 44(8), 441\u2013467 (2020)","journal-title":"J. Med. Eng. Technol."},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Kitade, I., et al.: Kinematic, kinetic, and musculoskeletal modeling analysis of gait in patients with cervical myelopathy using a severity classification. Spine J. 20(7), 1096\u20131105 (2020)","DOI":"10.1016\/j.spinee.2020.01.014"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Kokkotis, C., Moustakidis, S., Papageorgiou, E., Giakas, G., Tsaopoulos, D.: Machine learning in knee osteoarthritis: a review. Osteoarthritis Cartilage Open, 100069 (2020)","DOI":"10.1016\/j.ocarto.2020.100069"},{"issue":"10","key":"19_CR16","doi-asserted-by":"publisher","first-page":"3329","DOI":"10.3390\/s18103329","volume":"18","author":"P Kozlow","year":"2018","unstructured":"Kozlow, P., Abid, N., Yanushkevich, S.: Gait type analysis using dynamic Bayesian networks. Sensors 18(10), 3329 (2018)","journal-title":"Sensors"},{"key":"19_CR17","unstructured":"MathWorks, I.: Heart sound classifier. https:\/\/la.mathworks.com\/matlabcentral\/ \/fileexchange\/65286-heart-sound-classifier (2021). Accessed 06 Apr 2021"},{"issue":"2","key":"19_CR18","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/TNSRE.2009.2032638","volume":"18","author":"SS Nair","year":"2009","unstructured":"Nair, S.S., French, R.M., Laroche, D., Thomas, E.: The application of machine learning algorithms to the analysis of electromyographic patterns from arthritic patients. IEEE Trans. Neural Syst. Rehab. Eng. 18(2), 174\u2013184 (2009)","journal-title":"IEEE Trans. Neural Syst. Rehab. Eng."},{"issue":"1","key":"19_CR19","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.patcog.2013.06.028","volume":"47","author":"TT Ngo","year":"2014","unstructured":"Ngo, T.T., Makihara, Y., Nagahara, H., Mukaigawa, Y., Yagi, Y.: The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication. Pattern Recogn. 47(1), 228\u2013237 (2014)","journal-title":"Pattern Recogn."},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Paluszek, M., Thomas, S.: MATLAB Machine Learning. Apress, New York (2016)","DOI":"10.1007\/978-1-4842-2250-8"},{"key":"19_CR21","unstructured":"Physiopedia: 10 metre walk test. https:\/\/physio-pedia.com.html (2021). Accessed 19 June 2021"},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Recher, F., Banos, O., Nikamp, C.D., Schaake, L., Baten, C.T., Buurkc, J.H.: Optimizing activity recognition in stroke survivors for wearable exoskeletons. In: 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 173\u2013178. IEEE (2018)","DOI":"10.1109\/BIOROB.2018.8487740"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Rong, L., Jianzhong, Z., Ming, L., Xiangfeng, H.: A wearable acceleration sensor system for gait recognition. In: 2007 2nd IEEE Conference on Industrial Electronics and Applications, pp. 2654\u20132659. IEEE (2007)","DOI":"10.1109\/ICIEA.2007.4318894"},{"issue":"11","key":"19_CR24","doi-asserted-by":"publisher","first-page":"2542","DOI":"10.3390\/s19112542","volume":"19","author":"S Sharif Bidabadi","year":"2019","unstructured":"Sharif Bidabadi, S., Tan, T., Murray, I., Lee, G.: Tracking foot drop recovery following lumbar-spine surgery, applying multiclass gait classification using machine learning techniques. Sensors 19(11), 2542 (2019)","journal-title":"Sensors"},{"key":"19_CR25","doi-asserted-by":"publisher","first-page":"70497","DOI":"10.1109\/ACCESS.2018.2879896","volume":"6","author":"JP Singh","year":"2018","unstructured":"Singh, J.P., Jain, S., Arora, S., Singh, U.P.: Vision-based gait recognition: a survey. IEEE Access 6, 70497\u201370527 (2018)","journal-title":"IEEE Access"},{"issue":"5","key":"19_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3230633","volume":"51","author":"C Wan","year":"2018","unstructured":"Wan, C., Wang, L., Phoha, V.V.: A survey on gait recognition. ACM Comput. Surv. (CSUR) 51(5), 1\u201335 (2018)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"19_CR27","unstructured":"Whittle, M.W.: Gait Analysis: An Introduction. Butterworth-Heinemann, UK (2014)"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Zhan, A., et al.: Using smartphones and machine learning to quantify Parkinson disease severity: the mobile Parkinson disease score. JAMA Neurol. 75(7), 876\u2013880 (2018)","DOI":"10.1001\/jamaneurol.2018.0809"}],"container-title":["Lecture Notes in Computer Science","Advances in Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89820-5_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T00:58:46Z","timestamp":1634864326000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89820-5_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030898199","9783030898205"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89820-5_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micai.org\/2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"129","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}