{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:11:28Z","timestamp":1774627888167,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council, Taiwan","award":["NSTC 111-2221-E-324-003-MY3"],"award-info":[{"award-number":["NSTC 111-2221-E-324-003-MY3"]}]},{"name":"National Science and Technology Council, Taiwan","award":["NSTC 112-2221-E-214-013"],"award-info":[{"award-number":["NSTC 112-2221-E-214-013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gait analysis has been studied over the last few decades as the best way to objectively assess the technical outcome of a procedure designed to improve gait. The treating physician can understand the type of gait problem, gain insight into the etiology, and find the best treatment with gait analysis. The gait parameters are the kinematics, including the temporal and spatial parameters, and lack the activity information of skeletal muscles. Thus, the gait analysis measures not only the three-dimensional temporal and spatial graphs of kinematics but also the surface electromyograms (sEMGs) of the lower limbs. Now, the shoe-worn GaitUp Physilog\u00ae wearable inertial sensors can easily measure the gait parameters when subjects are walking on the general ground. However, it cannot measure muscle activity. The aim of this study is to measure the gait parameters using the sEMGs of the lower limbs. A self-made wireless device was used to measure the sEMGs from the vastus lateralis and gastrocnemius muscles of the left and right feet. Twenty young female subjects with a skeletal muscle index (SMI) below 5.7 kg\/m2 were recruited for this study and examined by the InBody 270 instrument. Four parameters of sEMG were used to estimate 23 gait parameters. They were measured using the GaitUp Physilog\u00ae wearable inertial sensors with three machine learning models, including random forest (RF), decision tree (DT), and XGBoost. The results show that 14 gait parameters could be well-estimated, and their correlation coefficients are above 0.800. This study signifies a step towards a more comprehensive analysis of gait with only sEMGs.<\/jats:p>","DOI":"10.3390\/s24030734","type":"journal-article","created":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T04:54:01Z","timestamp":1706072041000},"page":"734","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3923-4387","authenticated-orcid":false,"given":"Shing-Hong","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi-En","family":"Ting","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia-Jung","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, I-Shou University, Kaohsiung 82445, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun-Ju","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Golden-Ager Industry Management, Chaoyang University of Technology, Taichung City 41349, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7938-9033","authenticated-orcid":false,"given":"Wenxi","family":"Chen","sequence":"additional","affiliation":[{"name":"Division of Information Systems, School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu City 965-8580, Fukushima, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4964-6403","authenticated-orcid":false,"given":"Alok Kumar","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dorschky, E., Nitschke, M., Martindale, C.F., van den Bogert, A.J., Koelewijn, A.D., and Eskofier, B.M. (2020). CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics from Measured and Simulated Inertial Sensor Data. Front. Bioeng. Biotechnol., 8.","DOI":"10.3389\/fbioe.2020.00604"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kim, J.-K., Bae, M.-N., Lee, K.B., and Hong, S.G. (2021). Identification of Patients with Sarcopenia Using Gait Parameters Based on Inertial Sensors. Sensors, 21.","DOI":"10.3390\/s21051786"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dubois, A., and Charpillet, F. (2014, January 26\u201330). A Gait Analysis Method Based on a Depth Camera for Fall Prevention. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6944627"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/S0966-6362(97)01118-1","article-title":"Gait in the Elderly","volume":"5","author":"Prince","year":"1997","journal-title":"Gait Posture"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3362","DOI":"10.3390\/s140203362","article-title":"Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications","volume":"14","year":"2014","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.clnu.2012.02.007","article-title":"Sarcopenia as a Risk Factor for Falls in Elderly Individuals: Results from the IlSIRENTE Study","volume":"31","author":"Landi","year":"2012","journal-title":"Clin. Nutr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102807","DOI":"10.1016\/j.jelekin.2023.102807","article-title":"Tutorial. Surface Electromyogram (SEMG) Amplitude Estimation: Best Practices","volume":"72","author":"Clancy","year":"2023","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1109\/JBHI.2013.2286408","article-title":"The Progression of Muscle Fatigue During Exercise Estimation with the Aid of High-Frequency Component Parameters Derived From Ensemble Empirical Mode Decomposition","volume":"18","author":"Liu","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"994","DOI":"10.3389\/fneur.2020.00994","article-title":"Surface Electromyography Applied to Gait Analysis: How to Improve Its Impact in Clinics?","volume":"11","author":"Agostini","year":"2020","journal-title":"Front. Neurol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1038\/s41597-023-02263-3","article-title":"Surface Electromyogram, Kinematic, and Kinetic Dataset of Lower Limb Walking for Movement Intent Recognition","volume":"10","author":"Wei","year":"2023","journal-title":"Sci. Data"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s12984-020-0645-2","article-title":"Age-Specific Differences in the Time-Frequency Representation of Surface Electromyographic Data Recorded during a Submaximal Cyclic Back Extension Exercise: A Promising Biomarker to Detect Early Signs of Sarcopenia","volume":"17","author":"Habenicht","year":"2020","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.3233\/JIFS-179549","article-title":"Visualization of Activated Muscle Area Based on SEMG","volume":"38","author":"Cheng","year":"2020","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"222","DOI":"10.5435\/00124635-200205000-00009","article-title":"A Practical Guide to Gait Analysis","volume":"10","author":"Chambers","year":"2002","journal-title":"J. Am. Acad. Orthop. Surg."},{"key":"ref_14","first-page":"64","article-title":"Gait Analysis","volume":"39","author":"Chester","year":"2005","journal-title":"Biomed. Instrum. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.gaitpost.2021.09.203","article-title":"Validation of Shoe-Worn Gait Up Physilog\u00ae5 Wearable Inertial Sensors in Adolescents","volume":"91","author":"Carroll","year":"2022","journal-title":"Gait Posture"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.gaitpost.2012.07.012","article-title":"Quantitative Estimation of Foot-Flat and Stance Phase of Gait Using Foot-Worn Inertial Sensors","volume":"37","author":"Mariani","year":"2013","journal-title":"Gait Posture"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M.A., Shambour, M.K.Y., Alsalibi, A.I., and Gandomi, A.H. (2022). Machine learning in medical applications: A review of state-of-the-art methods. Comput. Biol. Med., 145.","DOI":"10.1016\/j.compbiomed.2022.105458"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Magoulas, G.D., and Prentza, A. (2001). Machine Learning in Medical Applications, SpringLink.","DOI":"10.1007\/3-540-44673-7_19"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, S.-H., Liu, L.-J., Pan, K.-L., Chen, W., and Tan, T.-H. (2019). Using the Characteristics of Pulse Waveform to Enhance the Accuracy of Blood Pressure Measurement by a Multi-Dimension Regression Model. Appl. Sci., 9.","DOI":"10.3390\/app9142922"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.compind.2017.04.003","article-title":"Continuous Blood Pressure Estimation Based on Multiple Parameters from Eletrocardiogram and Photoplethysmogram by Back-Propagation Neural Network","volume":"89","author":"Xu","year":"2017","journal-title":"Comput. Ind."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, X., and Park, S. (2021). An Inverse Relation between Hyperglycemia and Skeletal Muscle Mass Predicted by Using a Machine Learning Approach in Middle-Aged and Older Adults in Large Cohorts. J. Clin. Med., 10.","DOI":"10.3390\/jcm10102133"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pouladzadeh, P., Kuhad, P., Peddi, S.V.B., Yassine, A., and Shirmohammadi, S. (2016, January 23\u201326). Food Calorie Measurement Using Deep Learning Neural Network. Proceedings of the 2016 IEEE International Instrumentation and Measurement Technology Conference, Taipei, Taiwan.","DOI":"10.1109\/I2MTC.2016.7520547"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ruede, R., Heusser, V., Frank, L., Roitberg, A., Haurilet, M., and Stiefelhagen, R. (2021, January 10\u201315). Multi-Task Learning for Calorie Prediction on a Novel Large-Scale Recipe Dataset Enriched with Nutritional Information. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412839"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3389\/fams.2017.00014","article-title":"A Deep Learning Approach to Diabetic Blood Glucose Prediction","volume":"3","author":"Mhaskar","year":"2017","journal-title":"Front. Appl. Math. Stat."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, S.-H., Yang, Z.-K., Pan, K.-L., Zhu, X., and Chen, W. (2022). Estimation of Left Ventricular Ejection Fraction Using Cardiovascular Hemodynamic Parameters and Pulse Morphological Characteristics with Machine Learning Algorithms. Nutrients, 14.","DOI":"10.3390\/nu14194051"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/RBME.2018.2810957","article-title":"A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment","volume":"11","author":"Satija","year":"2018","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chowdhury, M.H., Shuzan, M.N.I., Chowdhury, M.E.H., Mahbub, Z.B., Uddin, M.M., Khandakar, A., and Reaz, M.B.I. (2020). Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques. Sensors, 20.","DOI":"10.3390\/s20113127"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, S.-H., Wang, J.-J., Chen, W., Pan, K.-L., and Su, C.-H. (2020). Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement. Appl. Sci., 10.","DOI":"10.3390\/app10041476"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sraitih, M., Jabrane, Y., and Hajjam El Hassani, A. (2021). An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques. J. Clin. Med., 10.","DOI":"10.3390\/jcm10225450"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dubois, A., Bihl, T., and Bresciani, J.-P. (2021). Identifying Fall Risk Predictors by Monitoring Daily Activities at Home Using a Depth Sensor Coupled to Machine Learning Algorithms. Sensors, 21.","DOI":"10.3390\/s21061957"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/MS.2016.114","article-title":"Machine Learning","volume":"33","author":"Louridas","year":"2016","journal-title":"IEEE Softw."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"35365","DOI":"10.1109\/ACCESS.2018.2836950","article-title":"Machine Learning and Deep Learning Methods for Cybersecurity","volume":"6","author":"Xin","year":"2018","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1001\/jama.2017.18391","article-title":"Big Data and Machine Learning in Health Care","volume":"319","author":"Beam","year":"2018","journal-title":"JAMA"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1056\/NEJMp1702071","article-title":"Machine Learning and Prediction in Medicine\u2014Beyond the Peak of Inflated Expectations","volume":"376","author":"Chen","year":"2017","journal-title":"N. Engl. J. Med."},{"key":"ref_35","unstructured":"(2023, April 02). Gait Up SA Physilog\u2014Digital Motion Analysis Platform. Available online: https:\/\/physilog.com."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, S.-H., Lin, C.-B., Chen, Y., Chen, W., Huang, T.-S., and Hsu, C.-Y. (2019). An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise. Sensors, 19.","DOI":"10.3390\/s19143108"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A Theory for Multiresolution Signal Decomposition: The Wavelet Representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","unstructured":"Phinyomark, A., Thongpanja, S., Hu, H., Phukpattaranont, P., and Limsakul, C. (2012). Computational Intelligence in Electromyography Analysis\u2014A Perspective on Current Applications and Future Challenges, InTech."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Aghamohammadi-Sereshki, A., Bayazi, M.-J.D., Ghomsheh, F.T., and Amirabdollahian, F. (2019, January 24\u201328). Investigation of Fatigue Using Different EMG Features. Proceedings of the 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), Toronto, ON, Canada.","DOI":"10.1109\/ICORR.2019.8779402"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1016\/j.jelekin.2012.06.005","article-title":"Sample Entropy Analysis of Surface EMG for Improved Muscle Activity Onset Detection against Spurious Background Spikes","volume":"22","author":"Zhang","year":"2012","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1046\/j.1365-201X.1997.00168.x","article-title":"Differential Responses in Intramuscular Pressure and EMG Fatigue Indicators during Low- vs. High-level Isometric Contractions to Fatigue","volume":"160","author":"Crenshaw","year":"1997","journal-title":"Acta Physiol. Scand."},{"key":"ref_42","first-page":"1","article-title":"Body Composition Assessment: A Comparison of the DXA, InBody 270, and Omron","volume":"3","author":"Czartoryski","year":"2020","journal-title":"J. Exerc. Nutr."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s10462-011-9272-4","article-title":"Decision Trees: A Recent Overview","volume":"39","author":"Kotsiantis","year":"2013","journal-title":"Artif. Intell. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1038\/nbt0908-1011","article-title":"What Are Decision Trees?","volume":"26","author":"Kingsford","year":"2008","journal-title":"Nat. Biotechnol."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Gramage, J., Albiach, J.P., Molt\u00f3, I.N., Amer-Cuenca, J.J., Huesa Moreno, V., and Segura-Ort\u00ed, E. (2020). A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes. Sensors, 20.","DOI":"10.3390\/s20216388"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"109520","DOI":"10.1016\/j.petrol.2021.109520","article-title":"An Optimized XGBoost Method for Predicting Reservoir Porosity Using Petrophysical Logs","volume":"208","author":"Pan","year":"2022","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"XGBoost: A Scalable Tree Boosting System","volume":"Volume 42","author":"Chen","year":"2016","journal-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1057\/jt.2009.5","article-title":"The Correlation Coefficient: Its Values Range Between +1\/\u22121, or Do They?","volume":"17","author":"Ratner","year":"2009","journal-title":"J. Target. Meas. Anal. Mark."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1093\/bioinformatics\/btq134","article-title":"Permutation Importance: A Corrected Feature Importance Measure","volume":"26","author":"Altmann","year":"2010","journal-title":"Bioinformatics"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019, January 4\u20138). Optuna: A Next-Generation Hyperparameter Optimization Framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1006\/jmps.1999.1279","article-title":"Cross-Validation Methods","volume":"44","author":"Browne","year":"2000","journal-title":"J. Math. Psychol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1080\/00140138108924919","article-title":"A Review of: \u201cHuman Walking\u201d. By V.T. Inman, H.J. Ralston and F. Todd. (Baltimore, London: Williams & Wilkins, 1981.) [Pp.154.]","volume":"24","author":"Pheasant","year":"1981","journal-title":"Ergonomics"},{"key":"ref_54","first-page":"46","article-title":"Review on the Detection of Multiple Neuromuscular Disorder Using Electromyography","volume":"1","author":"Ismail","year":"2023","journal-title":"South. J. Eng. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.gaitpost.2016.09.021","article-title":"Validation of a Commercial Inertial Sensor System for Spatiotemporal Gait Measurements in Children","volume":"51","author":"Lanovaz","year":"2017","journal-title":"Gait Posture"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1186\/s12984-015-0079-4","article-title":"Does Texting While Walking Really Affect Gait in Young Adults?","volume":"12","author":"Agostini","year":"2015","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/TNSRE.2019.2903687","article-title":"Asymmetry Index in Muscle Activations","volume":"27","author":"Castagneri","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.clinbiomech.2008.08.003","article-title":"Surface Electromyography and Muscle Force: Limits in SEMG\u2013Force Relationship and New Approaches for Applications","volume":"24","author":"Rau","year":"2009","journal-title":"Clin. Biomech."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/734\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:48:01Z","timestamp":1760104081000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/734"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,23]]},"references-count":58,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24030734"],"URL":"https:\/\/doi.org\/10.3390\/s24030734","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,23]]}}}