{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T21:23:16Z","timestamp":1775856196372,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T00:00:00Z","timestamp":1620172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Postural control decreases with aging. Thus, an efficient and accurate method of detecting postural control is needed. We enrolled 35 elderly adults (aged 82.06 \u00b1 8.74 years) and 20 healthy young adults (aged 21.60 \u00b1 0.60 years) who performed standing tasks for 40 s, performed six times. The coordinates of 15 joint nodes were captured using a Kinect device (30 Hz). We plotted joint positions into a single 2D figure (named a joint\u2013node plot, JNP) once per second for up to 40 s. A total of 15 methods combining deep and machine learning for postural control classification were investigated. The accuracy, sensitivity, specificity, positive predicted value (PPV), negative predicted value (NPV), and kappa values of the selected methods were assessed. The highest PPV, NPV, accuracy, sensitivity, specificity, and kappa values were higher than 0.9 in validation testing. The presented method using JNPs demonstrated strong performance in detecting the postural control ability of young and elderly adults.<\/jats:p>","DOI":"10.3390\/s21093212","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T22:51:42Z","timestamp":1620255102000},"page":"3212","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Detection of Postural Control in Young and Elderly Adults Using Deep and Machine Learning Methods with Joint\u2013Node Plots"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3914-1049","authenticated-orcid":false,"given":"Posen","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Occupation Therapy, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung 82445, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3348-4422","authenticated-orcid":false,"given":"Tai-Been","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung 82445, Taiwan"},{"name":"Institute of Statistics, National Yang Ming Chiao Tung University, No.  1001, University Rd., Hsinchu 30010, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6937-5140","authenticated-orcid":false,"given":"Chi-Yuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung 82445, Taiwan"},{"name":"Department of Radiology, Zuoying Branch of Kaohsiung Armed Forces General Hospital, No. 553, Junxiao Rd., Zuoying District, Kaohsiung 81342, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1175-0531","authenticated-orcid":false,"given":"Shih-Yen","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung 82445, Taiwan"},{"name":"Department of Information Engineering, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung 82445, Taiwan"}]},{"given":"Chin-Hsuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Occupation Therapy, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung 82445, Taiwan"},{"name":"Department of Occupational Therapy, Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, No. 130, Kaisyuan 2nd Rd., Lingya District, Kaohsiung 80276, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1093\/ptj\/67.12.1881","article-title":"Clinical Measurement of Postural Control in Adults","volume":"67","author":"Horak","year":"1987","journal-title":"Phys. Ther."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.jbiomech.2018.01.029","article-title":"Balance assessment during squatting exercise: A comparison between laboratory grade force plate and a commercial, low-cost device","volume":"71","author":"Mengarelli","year":"2018","journal-title":"J. Biomech."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Koltermann, J.J., Gerber, M., Beck, H., and Beck, M. (2017). Validation of the HUMAC Balance System in Comparison with Conventional Force Plates. Technologies, 5.","DOI":"10.3390\/technologies5030044"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"18244","DOI":"10.3390\/s141018244","article-title":"Validating and Calibrating the Nintendo Wii Balance Board to Derive Reliable Center of Pressure Measures","volume":"14","author":"Leach","year":"2014","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.medengphy.2019.10.018","article-title":"Validity of using wearable inertial sensors for assessing the dynamics of standing balance","volume":"77","author":"Noamani","year":"2020","journal-title":"Med. Eng. Phys."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Maudsley-Barton, S., Hoon Yap, M., Bukowski, A., Mills, R., and McPhee, J. (2020). A new process to measure postural sway using a kinect depth camera during a sensory organisation test. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0227485"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/JSEN.2013.2296509","article-title":"Reliability and validity of Kinect RGB-D sensor for assessing standing balance","volume":"14","author":"Yang","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"16955","DOI":"10.3390\/s140916955","article-title":"Whole body center of mass estimation with portable sensors: Using the statically equivalent serial chain and a kinect","volume":"14","author":"Hayashibe","year":"2014","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s40520-014-0253-8","article-title":"Investigation of key factors affecting the balance function of older adults","volume":"27","author":"Pu","year":"2015","journal-title":"Aging Clin. Exp. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.gaitpost.2015.03.005","article-title":"Reliability and concurrent validity of the Microsoft Xbox One Kinect for assessment of standing balance and postural control","volume":"42","author":"Clark","year":"2015","journal-title":"Gait Posture"},{"key":"ref_11","first-page":"1077","article-title":"Use of the Microsoft Kinect system to characterize balance ability during balance training","volume":"10","author":"Lim","year":"2015","journal-title":"Clin. Interv. Aging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.apergo.2015.01.005","article-title":"The validity of the first and second generation Microsoft Kinect\u2122 for identifying joint center locations during static postures","volume":"49","author":"Xu","year":"2015","journal-title":"Appl. Ergon."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.neucom.2015.12.128","article-title":"Evaluation of Kinect2 based balance measurement","volume":"208","author":"Lv","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.pmr.2018.12.006","article-title":"Validity and reliability of the Kinect for assessment of standardized transitional movements and balance: Systematic review and translation into practice","volume":"30","author":"Puh","year":"2019","journal-title":"Phys. Med. Rehabil. Clin."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"439","DOI":"10.5370\/KIEE.2017.66.2.439","article-title":"Evaluation of balance ability of the elderly using kinect sensor","volume":"66","author":"Yang","year":"2017","journal-title":"Trans. Korean Inst. Electr. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1177\/0269215517730117","article-title":"An investigation of the use of the Kinect system as a measure of dynamic balance and forward reach in the elderly","volume":"32","author":"Hsiao","year":"2018","journal-title":"Clin. Rehabil."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Springer, S., and Yogev Seligmann, G. (2016). Validity of the Kinect for Gait Assessment: A Focused Review. Sensors, 16.","DOI":"10.3390\/s16020194"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.3390\/s150101785","article-title":"Pose Estimation with a Kinect for Ergonomic Studies: Evaluation of the Accuracy Using a Virtual Mannequin","volume":"15","author":"Plantard","year":"2015","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, C.-H., Lee, P., Chen, Y.-L., Yen, C.-W., and Yu, C.-W. (2020). Study of Postural Stability Features by Using Kinect Depth Sensors to Assess Body Joint Coordination Patterns. Sensors, 20.","DOI":"10.3390\/s20051291"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.gaitpost.2021.01.005","article-title":"Simplified Digital Balance Assessment in Typically Developing School Children","volume":"84","author":"Heidt","year":"2021","journal-title":"Gait Posture"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"O\u00f1a, E.D., Jard\u00f3n, A., and Balaguer, C. (2020, January 12\u201314). Automatic Assessment of Arm Motor Function and Postural Stability in Virtual Scenarios: Towards a Virtual Version of the Fugl-Meyer Test. Proceedings of the 2020 IEEE 8th International Conference on Serious Games and Applications for Health (SeGAH), Vancouver, BC, Canada.","DOI":"10.1109\/SeGAH49190.2020.9201758"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Otte, K., Kayser, B., Mansow-Model, S., Verrel, J., Paul, F., Brandt, A.U., and Schmitz-H\u00fcbsch, T. (2016). Accuracy and Reliability of the Kinect Version 2 for Clinical Measurement of Motor Function. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0166532"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/JBHI.2017.2686330","article-title":"Validation of static and dynamic balance assessment using Microsoft Kinect for young and elderly populations","volume":"22","author":"Eltoukhy","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.gaitpost.2017.09.010","article-title":"Kinect-based assessment of lower limb kinematics and dynamic postural control during the star excursion balance test","volume":"58","author":"Eltoukhy","year":"2017","journal-title":"Gait Posture"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hayashibe, M., Gonz\u00e1lez, A., and Tournier, M. (2020, January 20\u201324). Personalized balance and fall risk visualization with Kinect Two. Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175330"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.injury.2014.08.047","article-title":"Commercially available gaming systems as clinical assessment tools to improve value in the orthopaedic setting: A systematic review","volume":"46","author":"Ruff","year":"2015","journal-title":"Injury"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bakator, M., and Radosav, D. (2018). Deep learning and medical diagnosis: A review of literature. Multimodal Technol. Interact., 2.","DOI":"10.3390\/mti2030047"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/978-3-030-26773-5_16","article-title":"A Predictive Model for MicroRNA Expressions in Pediatric Multiple Sclerosis Detection","volume":"11676","author":"Torra","year":"2019","journal-title":"Modeling Decisions for Artificial Intelligence"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","article-title":"Deep Learning in Medical Imaging: General Overview","volume":"18","author":"Lee","year":"2017","journal-title":"Korean J. Radiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s12194-017-0406-5","article-title":"Overview of deep learning in medical imaging","volume":"10","author":"Suzuki","year":"2017","journal-title":"Radiol. Phys. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/JBHI.2016.2636665","article-title":"Deep learning for health informatics","volume":"21","author":"Wong","year":"2017","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1021\/acs.molpharmaceut.5b00982","article-title":"Applications of Deep Learning in Biomedicine","volume":"13","author":"Mamoshina","year":"2016","journal-title":"Mol. Pharm."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.26599\/BDMA.2018.9020001","article-title":"Applications of deep learning to MRI images: A survey","volume":"1","author":"Liu","year":"2018","journal-title":"Big Data Mining Anal."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","article-title":"Deep learning in medical ultrasound analysis: A review","volume":"5","author":"Liu","year":"2019","journal-title":"Engineering"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1002\/mp.13264","article-title":"Deep learning in medical imaging and radiation therapy","volume":"46","author":"Sahiner","year":"2019","journal-title":"Med. Phys."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1550147717703257","DOI":"10.1177\/1550147717703257","article-title":"Fall prediction based on biomechanics equilibrium using Kinect","volume":"13","author":"Tao","year":"2017","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1840005","DOI":"10.1142\/S0219691318400052","article-title":"Elders\u2019 fall detection based on biomechanical features using depth camera","volume":"16","author":"Xu","year":"2018","journal-title":"Int. J. Wavelets Multiresolut. Inf. Process."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Vonstad, E.K., Su, X., Vereijken, B., Bach, K., and Nilsen, J.H. (2020). Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training. Sensors, 20.","DOI":"10.3390\/s20236940"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ma, Y., Liu, D., and Cai, L. (2020). Deep Learning-Based Upper Limb Functional Assessment Using a Single Kinect v2 Sensor. Sensors, 20.","DOI":"10.3390\/s20071903"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hsu, S.Y., Yeh, L.R., Chen, T.B., Du, W.C., Huang, Y.H., Twan, W.H., Lin, M.C., Hsu, Y.H., Wu, Y.C., and Chen, H.Y. (2020). Classification of the Multiple Stages of Parkinson\u2019s Disease by a Deep Convolution Neural Network Based on 99mTc-TRODAT-1 SPECT Images. Molecules, 19.","DOI":"10.3390\/molecules25204792"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.jmir.2019.09.005","article-title":"Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging","volume":"50","author":"Currie","year":"2019","journal-title":"J. Med. Imaging Radiat. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"29S","DOI":"10.2967\/jnumed.118.220590","article-title":"Artificial Intelligence in Nuclear Medicine","volume":"60","author":"Nensa","year":"2019","journal-title":"J. Nucl. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1055\/s-0039-1677908","article-title":"AI in Health: State of the Art, Challenges, and Future Directions","volume":"28","author":"Wang","year":"2019","journal-title":"Yearb. Med. Inform."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"04020022","DOI":"10.1061\/JPEODX.0000175","article-title":"Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems","volume":"146","author":"Piryonesi","year":"2020","journal-title":"J. Transp. Eng. Part B Pavements"},{"key":"ref_45","first-page":"725","article-title":"Could Postural Strategies Be Assessed with the Microsoft Kinect v2?","volume":"Volume 68","author":"Lhotska","year":"2019","journal-title":"World Congress on Medical Physics and Biomedical Engineering 2018"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bemal, V.E., Satterthwaite, N.A., Napoli, A., Glass, S.M., Tucker, C.A., and Obeid, I. (2017, January 2). Kinect v2 accuracy as a body segment measuring tool. Proceedings of the2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, USA.","DOI":"10.1109\/SPMB.2017.8257050"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"133","DOI":"10.4316\/AECE.2014.01020","article-title":"Automatic assessing of tremor severity using nonlinear dynamics, artificial neural networks and neuro-fuzzy classifier","volume":"14","author":"Geman","year":"2014","journal-title":"Adv. Electr. Comput. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"113","DOI":"10.3233\/JPD-191758","article-title":"Technology-based objective measures detect subclinical axial signs in untreated, de novo Parkinson\u2019s disease","volume":"10","author":"Ricci","year":"2020","journal-title":"J. Parkinson\u2019s Dis."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Buongiorno, D., Bortone, I., Cascarano, G.D., Trotta, G.F., Brunetti, A., and Bevilacqua, V. (2019). A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson\u2019s Disease. BMC Med. Inform. Decis. Mak., 19.","DOI":"10.1186\/s12911-019-0987-5"},{"key":"ref_50","first-page":"1","article-title":"The detection of age groups by dynamic gait outcomes using machine learning approaches","volume":"10","author":"Zhou","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1007\/s40520-018-1036-4","article-title":"Coordination of trunk and foot acceleration during gait is affected by walking velocity and fall history in elderly adults","volume":"31","author":"Craig","year":"2019","journal-title":"Aging Clin. Exp. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.humov.2019.05.018","article-title":"Altered visual and somatosensory feedback affects gait stability in persons with multiple sclerosis","volume":"66","author":"Craig","year":"2019","journal-title":"Hum. Mov. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/TNSRE.2019.2891000","article-title":"Automatically evaluating balance: A machine learning approach","volume":"27","author":"Bao","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"323","DOI":"10.3233\/VES-190683","article-title":"Effects of long-term vestibular rehabilitation therapy with vibrotactile sensory augmentation for people with unilateral vestibular disorders\u2014A randomized preliminary study","volume":"29","author":"Bao","year":"2019","journal-title":"J. Vestib. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1159\/000481454","article-title":"Effects of Wearable Sensor-Based Balance and Gait Training on Balance, Gait, and Functional Performance in Healthy and Patient Populations: A Systematic Review and Meta-Analysis of Randomized Controlled Trials","volume":"64","author":"Gordt","year":"2018","journal-title":"Gerontology"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"705","DOI":"10.3233\/WOR-193032","article-title":"Stumbling prediction based on plantar pressure distribution","volume":"64","author":"Niu","year":"2019","journal-title":"Work"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.gaitpost.2018.01.036","article-title":"Effects of high heeled shoes on gait. A review","volume":"61","author":"Wiedemeijer","year":"2018","journal-title":"Gait Posture"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Lerebourg, L., L\u2019Hermette, M., Menez, C., and Coquart, J. (2020). The effects of shoe type on lower limb venous status during gait or exercise: A systematic review. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0239787"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.rehab.2019.08.003","article-title":"Accuracy of modified 30-s chair-stand test for predicting falls in older adults","volume":"63","author":"Roongbenjawan","year":"2020","journal-title":"Ann. Phys. Rehabil. Med."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1142\/S1013702520500134","article-title":"The effect of the type of foam pad used in the modified Clinical Test of Sensory Interaction and Balance (mCTSIB) on the accuracy in identifying older adults with fall history","volume":"40","author":"Boonsinsukh","year":"2020","journal-title":"Hong Kong Physiother. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/9\/3212\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:57:23Z","timestamp":1760162243000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/9\/3212"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,5]]},"references-count":60,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["s21093212"],"URL":"https:\/\/doi.org\/10.3390\/s21093212","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,5]]}}}