{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:48:32Z","timestamp":1779151712810,"version":"3.51.4"},"reference-count":61,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T00:00:00Z","timestamp":1720742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korean government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health and Welfare, the Ministry of Food and Drug Safety)","award":["RS-2022-00141121"],"award-info":[{"award-number":["RS-2022-00141121"]}]},{"name":"Korean government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health and Welfare, the Ministry of Food and Drug Safety)","award":["P0018445"],"award-info":[{"award-number":["P0018445"]}]},{"name":"Korea Institute for Advancement of Technology (KIAT)","award":["RS-2022-00141121"],"award-info":[{"award-number":["RS-2022-00141121"]}]},{"name":"Korea Institute for Advancement of Technology (KIAT)","award":["P0018445"],"award-info":[{"award-number":["P0018445"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate prediction of scoliotic curve progression is crucial for guiding treatment decisions in adolescent idiopathic scoliosis (AIS). Traditional methods of assessing the likelihood of AIS progression are limited by variability and rely on static measurements. This study developed and validated machine learning models for classifying progressive and non-progressive scoliotic curves based on gait analysis using wearable inertial sensors. Gait data from 38 AIS patients were collected using seven inertial measurement unit (IMU) sensors, and hip\u2013knee (HK) cyclograms representing inter-joint coordination were generated. Various machine learning algorithms, including support vector machine (SVM), random forest (RF), and novel deep convolutional neural network (DCNN) models utilizing multi-plane HK cyclograms, were developed and evaluated using 10-fold cross-validation. The DCNN model incorporating multi-plane HK cyclograms and clinical factors achieved an accuracy of 92% in predicting curve progression, outperforming SVM (55% accuracy) and RF (52% accuracy) models using handcrafted gait features. Gradient-based class activation mapping revealed that the DCNN model focused on the swing phase of the gait cycle to make predictions. This study demonstrates the potential of deep learning techniques, and DCNNs in particular, in accurately classifying scoliotic curve progression using gait data from wearable IMU sensors.<\/jats:p>","DOI":"10.3390\/s24144504","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T08:16:57Z","timestamp":1720772217000},"page":"4504","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Explainable Deep-Learning-Based Gait Analysis of Hip\u2013Knee Cyclogram for the Prediction of Adolescent Idiopathic Scoliosis Progression"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9827-9833","authenticated-orcid":false,"given":"Yong-Gyun","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Rehabilitation Medicine, Hanyang University College of Medicine, Seoul 04763, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8862-6061","authenticated-orcid":false,"given":"Sungjoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, Hanyang University College of Medicine, Seoul 04763, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5619-4818","authenticated-orcid":false,"given":"Jae Hyeon","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, Hanyang University College of Medicine, Seoul 04763, Republic of Korea"},{"name":"Department of Rehabilitation Medicine, Hanyang University Guri Hospital, Guri 11923, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3380-6962","authenticated-orcid":false,"given":"Seung","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Hanyang University College of Medicine, Seoul 04763, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4530-5507","authenticated-orcid":false,"given":"Minkyu","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hanyang University College of Engineering, Seoul 04763, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7497-4797","authenticated-orcid":false,"given":"Yeo Joon","family":"Yun","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, Hanyang University Guri Hospital, Guri 11923, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9720-7950","authenticated-orcid":false,"given":"Jae-sung","family":"Cho","sequence":"additional","affiliation":[{"name":"Robotics Lab, Research and Development Division of Hyundai Motor Company, Uiwang 16082, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2144-9523","authenticated-orcid":false,"given":"Sungmin","family":"You","sequence":"additional","affiliation":[{"name":"Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"},{"name":"Division of Newborn Medicine, Boston Children\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0241-0954","authenticated-orcid":false,"given":"Seong-Ho","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, Hanyang University College of Medicine, Seoul 04763, Republic of Korea"},{"name":"Department of Rehabilitation Medicine, Hanyang University Guri Hospital, Guri 11923, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1001\/jama.2017.19342","article-title":"Screening for Adolescent Idiopathic Scoliosis: US Preventive Services Task Force Recommendation Statement","volume":"319","author":"Grossman","year":"2018","journal-title":"JAMA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15030","DOI":"10.1038\/nrdp.2015.30","article-title":"Adolescent idiopathic scoliosis","volume":"1","author":"Cheng","year":"2015","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_3","first-page":"1817","article-title":"Adolescent idiopathic scoliosis: Radiologic decision-making","volume":"65","author":"Greiner","year":"2002","journal-title":"Am. Fam. Physician"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1016\/S0140-6736(08)60658-3","article-title":"Adolescent idiopathic scoliosis","volume":"371","author":"Weinstein","year":"2008","journal-title":"Lancet"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s11832-012-0462-7","article-title":"Natural history of adolescent idiopathic scoliosis: A tool for guidance in decision of surgery of curves above 50\u00b0","volume":"7","author":"Danielsson","year":"2013","journal-title":"J. Child. Orthop."},{"key":"ref_6","first-page":"111","article-title":"Adolescent idiopathic scoliosis: Review and current concepts","volume":"64","author":"Reamy","year":"2001","journal-title":"Am. Fam. Physician"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.2106\/00004623-198466070-00013","article-title":"The prediction of curve progression in untreated idiopathic scoliosis during growth","volume":"66","author":"Lonstein","year":"1984","journal-title":"J. Bone Jt. Surg. Am."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Busscher, I., Wapstra, F.H., and Veldhuizen, A.G. (2010). Predicting growth and curve progression in the individual patient with adolescent idiopathic scoliosis: Design of a prospective longitudinal cohort study. BMC Musculoskelet. Disord., 11.","DOI":"10.1186\/1471-2474-11-93"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s40945-015-0001-1","article-title":"Scoliosis: Lower limb asymmetries during the gait cycle","volume":"5","author":"Haber","year":"2015","journal-title":"Arch. Physiother."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2407","DOI":"10.1007\/s00586-013-2845-y","article-title":"Asymmetrical gait in adolescents with idiopathic scoliosis","volume":"22","author":"Yang","year":"2013","journal-title":"Eur. Spine J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1972","DOI":"10.1007\/s00586-020-06361-3","article-title":"Curve location influences spinal balance in coronal and sagittal planes but not transversal trunk motion in adolescents with idiopathic scoliosis: A prospective observational study","volume":"29","author":"Pesenti","year":"2020","journal-title":"Eur. Spine J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1007\/s00586-015-3931-0","article-title":"Analysis of coordination between thoracic and pelvic kinematic movements during gait in adolescents with idiopathic scoliosis","volume":"25","author":"Park","year":"2016","journal-title":"Eur. Spine J."},{"key":"ref_13","first-page":"850","article-title":"Applying machine learning to gait analysis data for disease identification","volume":"210","author":"Joyseeree","year":"2015","journal-title":"Stud. Health Technol. Inf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"167830","DOI":"10.1109\/ACCESS.2020.3022818","article-title":"Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review","volume":"8","author":"Saboor","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","first-page":"1","article-title":"The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature","volume":"8","author":"Lim","year":"2022","journal-title":"Digit. Health"},{"key":"ref_16","unstructured":"Kozma, R., Alippi, C., Choe, Y., and Morabito, F.C. (2019). Evolving Deep Neural Networks. Artificial Intelligence in the Age of Neural Networks and Brain Computing, Academic Press."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fraiwan, M., Audat, Z., Fraiwan, L., and Manasreh, T. (2022). Using deep transfer learning to detect scoliosis and spondylolisthesis from x-ray images. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0267851"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"101220","DOI":"10.1016\/j.eclinm.2021.101220","article-title":"Application of deep learning upon spinal radiographs to predict progression in adolescent idiopathic scoliosis at first clinic visit","volume":"42","author":"Wang","year":"2021","journal-title":"eClinicalMedicine"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1007\/s00586-021-07025-6","article-title":"Comparison of manual versus automated measurement of Cobb angle in idiopathic scoliosis based on a deep learning keypoint detection technology","volume":"31","author":"Sun","year":"2022","journal-title":"Eur. Spine J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhang, K., Fan, H., Huang, Z., Xiang, Y., Yang, J., He, L., Zhang, L., Yang, Y., and Li, R. (2019). Development and validation of deep learning algorithms for scoliosis screening using back images. Commun. Biol., 2.","DOI":"10.1038\/s42003-019-0635-8"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1016\/j.spinee.2021.01.022","article-title":"An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection","volume":"21","author":"Kokabu","year":"2021","journal-title":"Spine J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ishikawa, Y., Kokabu, T., Yamada, K., Abe, Y., Tachi, H., Suzuki, H., Ohnishi, T., Endo, T., Ukeba, D., and Ura, K. (2023). Prediction of Cobb Angle Using Deep Learning Algorithm with Three-Dimensional Depth Sensor Considering the Influence of Garment in Idiopathic Scoliosis. J. Clin. Med., 12.","DOI":"10.3390\/jcm12020499"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yahara, Y., Tamura, M., Seki, S., Kondo, Y., Makino, H., Watanabe, K., Kamei, K., Futakawa, H., and Kawaguchi, Y. (2022). A deep convolutional neural network to predict the curve progression of adolescent idiopathic scoliosis: A pilot study. BMC Musculoskelet. Disord., 23.","DOI":"10.1186\/s12891-022-05565-6"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1080\/10255842.2022.2117547","article-title":"A preliminary study in classification of the severity of spine deformation in adolescents with lumbar\/thoracolumbar idiopathic scoliosis using machine learning algorithms based on lumbosacral joint efforts during gait","volume":"26","author":"Samadi","year":"2023","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/S0966-6362(98)00014-9","article-title":"A new gait parameterization technique by means of cyclogram moments: Application to human slope walking","volume":"8","author":"Goswami","year":"1998","journal-title":"Gait Posture"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1038\/s41598-020-80237-w","article-title":"Effects of knee osteoarthritis severity on inter-joint coordination and gait variability as measured by hip-knee cyclograms","volume":"11","author":"Park","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lee, H.S., Ryu, H., Lee, S.U., Cho, J.S., You, S., Park, J.H., and Jang, S.H. (2021). Analysis of Gait Characteristics Using Hip-Knee Cyclograms in Patients with Hemiplegic Stroke. Sensors, 21.","DOI":"10.3390\/s21227685"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"872","DOI":"10.5535\/arm.2018.42.6.872","article-title":"Evaluation of Validity and Reliability of Inertial Measurement Unit-Based Gait Analysis Systems","volume":"42","author":"Cho","year":"2018","journal-title":"Ann. Rehabil. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"415","DOI":"10.5535\/arm.20071","article-title":"Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model","volume":"44","author":"Yang","year":"2020","journal-title":"Ann. Rehabil. Med."},{"key":"ref_30","unstructured":"Madgwick, S.O.H. (2011). Automated Calibration of an Accelerometers, Magnetometers and Gyroscopes\u2014A Feasibility Study, x-io Technologies Limited."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"48","DOI":"10.14311\/1231","article-title":"A Study of Gait and Posture with the Use of Cyclograms","volume":"50","author":"Hajny","year":"2010","journal-title":"Acta Polytech."},{"key":"ref_32","first-page":"109","article-title":"Angle\u2014Angle Diagrams in the Assessment of Locomotion","volume":"59","author":"Hershler","year":"1980","journal-title":"Am. J. Phys. Med. Rehabil."},{"key":"ref_33","unstructured":"Zwillinger, D. (2018). CRC Standard Mathematical Tables and Formulae, CRC. [33rd ed.]."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1109\/3468.618255","article-title":"Application of majority voting to pattern recognition: An analysis of its behavior and performance","volume":"27","author":"Lam","year":"1997","journal-title":"IEEE Trans. Syst. Man Cybern.\u2014Part A Syst. Hum."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_38","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv."},{"key":"ref_39","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_40","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_41","unstructured":"Dillon, J., Langmore, I., Tran, D., Brevdo, E., Vasudevan, S., Moore, D., Patton, B., Alemi, A., Hoffman, M., and Saurous, R. (2017). TensorFlow Distributions. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1080\/03091902.2020.1822940","article-title":"Role of machine learning in gait analysis: A review","volume":"44","author":"Khera","year":"2020","journal-title":"J. Med. Eng. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Alfayeed, S.M., and Saini, B.S. (2021, January 17\u201318). Human Gait Analysis Using Machine Learning: A Review. Proceedings of the 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates.","DOI":"10.1109\/ICCIKE51210.2021.9410678"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Harris, E.J., Khoo, I.-H., and Demircan, E. (2022). A Survey of Human Gait-Based Artificial Intelligence Applications. Front. Robot. AI, 8.","DOI":"10.3389\/frobt.2021.749274"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2391","DOI":"10.1038\/s41598-019-38748-8","article-title":"Explaining the unique nature of individual gait patterns with deep learning","volume":"9","author":"Horst","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_47","first-page":"1499","article-title":"Recent use of deep learning techniques in clinical applications based on gait: A survey","volume":"8","author":"Matsushita","year":"2021","journal-title":"J. Comput. Des. Eng."},{"key":"ref_48","unstructured":"Samek, W., Wiegand, T., and M\u00fcller, K.-R. (2017). Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Taye, M.M. (2023). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 12.","DOI":"10.3390\/computers12050091"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1933","DOI":"10.1097\/01.brs.0000229230.68870.97","article-title":"Progression risk of idiopathic juvenile scoliosis during pubertal growth","volume":"31","author":"Charles","year":"2006","journal-title":"Spine"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1097\/BRS.0b013e31819c9431","article-title":"Curve progression in idiopathic scoliosis: Follow-up study to skeletal maturity","volume":"34","author":"Tan","year":"2009","journal-title":"Spine"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e633","DOI":"10.5435\/JAAOS-D-23-00062","article-title":"Breast Cancer Incidence, Mortality, and Cost in Adolescent Idiopathic Scoliosis Patients and the Role of Low Dose Biplanar Radiography","volume":"31","author":"Farivar","year":"2023","journal-title":"J. Am. Acad. Orthop. Surg."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/s00586-009-0913-0","article-title":"A review of methods for quantitative evaluation of spinal curvature","volume":"18","author":"Vrtovec","year":"2009","journal-title":"Eur. Spine J."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1097\/BRS.0000000000003316","article-title":"A Predictive Model of Progression for Adolescent Idiopathic Scoliosis Based on 3D Spine Parameters at First Visit","volume":"45","author":"Nault","year":"2020","journal-title":"Spine"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.compbiomed.2018.09.029","article-title":"Prediction of spinal curve progression in Adolescent Idiopathic Scoliosis using Random Forest regression","volume":"103","author":"Duong","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s12541-013-0106-y","article-title":"Determination of input variables for the development of a gait asymmetry expert system in patients with idiopathic scoliosis","volume":"14","author":"Choi","year":"2013","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.1016\/j.jbiomech.2004.02.047","article-title":"Quantification of human motion: Gait analysis-benefits and limitations to its application to clinical problems","volume":"37","author":"Simon","year":"2004","journal-title":"J. Biomech."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.1007\/s12541-018-0215-8","article-title":"Scoliosis Screening through a Machine Learning Based Gait Analysis Test","volume":"19","author":"Cho","year":"2018","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Liu, Y., Li, X., Dou, X., Huang, Z., Wang, J., Liao, B., and Zhang, X. (2022). Correlational analysis of three-dimensional spinopelvic parameters with standing balance and gait characteristics in adolescent idiopathic scoliosis: A preliminary research on Lenke V. Front. Bioeng. Biotechnol., 10.","DOI":"10.3389\/fbioe.2022.1022376"},{"key":"ref_60","unstructured":"Park, Y.S., Woo, B., Kim, J., Chae, W.-S., Kim, D.S., Jung, J.-H., Lee, C.-H., and Lim, Y.-T. (2012, January 2\u20136). Comparison of Gait Analysis between Adolescent Idiopathic Scoliosis Patients and Age Matched Controls. Proceedings of the 30th Annual Conference of Biomechanics in Sports, Melbourne, Australia."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zhang, Q., and Yang, Y. (2021). Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks. arXiv.","DOI":"10.1109\/CVPRW50498.2020.00020"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/14\/4504\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:15:30Z","timestamp":1760109330000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/14\/4504"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,12]]},"references-count":61,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24144504"],"URL":"https:\/\/doi.org\/10.3390\/s24144504","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,12]]}}}