{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T17:03:57Z","timestamp":1790960637224,"version":"4.1.0"},"reference-count":59,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T00:00:00Z","timestamp":1780704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","award":["RGPIN-2024-04808"],"award-info":[{"award-number":["RGPIN-2024-04808"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"award":["RGPIN-2024-04808"],"award-info":[{"award-number":["RGPIN-2024-04808"]}],"id":[{"id":"https:\/\/ror.org\/01h531d29","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Consumer-grade wearable sensors may enable continuous monitoring of pilot workload and stress during flight training, yet most prior studies rely on simulators, raw-score labelling, and within-subject validation, limiting generalisability. This study evaluates whether electrodermal activity (EDA), electrocardiogram (ECG)-derived features, and wrist skin temperature, recorded from an Empatica Embrace Plus and a Polar H10 during real Cessna 172 flight training, can classify pilots\u2019 task-relative workload and stress deviations. Thirty-five pilots completed four flight segments and rated workload and stress after each. Fold-safe two-way residual binary labels removed inter-pilot scale-use differences and task-level effects, and five classifiers were evaluated under leave-one-subject-out (LOSO) cross-validation with Benjamini\u2013Hochberg FDR correction. Under LOSO, a Linear SVC on combined features classified stress (macro F1 = 0.607) and XGBoost on EDA classified workload (macro F1 = 0.598) significantly above chance (padj=0.033); both remained stable under nested cross-validation with an inner hyperparameter search (nested 0.606 and 0.561). A LightGBM model on EDA gave a numerically higher stress score (0.611) that did not survive nested validation. Subject-dependent within-subject validation produced higher apparent performance (macro F1 = 0.853 for stress and 0.791 for workload), but a stricter within-pilot analysis was unstable. These contrasts indicate that personalised classification may be feasible after calibration, whereas uncalibrated cross-pilot prediction in real flight remains modest, with post-flight debriefing the most plausible near-term application.<\/jats:p>","DOI":"10.3390\/s26123627","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T05:43:46Z","timestamp":1780897426000},"page":"3627","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Consumer-Grade Wearable Sensors for Classifying Pilot Workload and Stress During Real Flight Training: A Leave-One-Subject-Out Validation Study"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7845-5265","authenticated-orcid":false,"given":"Rongbing","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"},{"name":"Waterloo Institute for Sustainable Aeronautics, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6448-6674","authenticated-orcid":false,"given":"Shi","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"},{"name":"Waterloo Institute for Sustainable Aeronautics, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2839-8562","authenticated-orcid":false,"given":"Michael","family":"Barnett-Cowan","sequence":"additional","affiliation":[{"name":"Waterloo Institute for Sustainable Aeronautics, University of Waterloo, Waterloo, ON N2L 3G1, Canada"},{"name":"Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth","family":"Irving","sequence":"additional","affiliation":[{"name":"Waterloo Institute for Sustainable Aeronautics, University of Waterloo, Waterloo, ON N2L 3G1, Canada"},{"name":"School of Optometry & Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ewa","family":"Niechwiej-Szwedo","sequence":"additional","affiliation":[{"name":"Waterloo Institute for Sustainable Aeronautics, University of Waterloo, Waterloo, ON N2L 3G1, Canada"},{"name":"Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8806-6730","authenticated-orcid":false,"given":"Suzanne","family":"Kearns","sequence":"additional","affiliation":[{"name":"Waterloo Institute for Sustainable Aeronautics, University of Waterloo, Waterloo, ON N2L 3G1, Canada"},{"name":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1518\/001872008X288394","article-title":"Multiple resources and mental workload","volume":"50","author":"Wickens","year":"2008","journal-title":"Hum. Factors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1207\/S15327108IJAP1201_2","article-title":"An analysis of mental workload in pilots during flight using multiple psychophysiological measures","volume":"12","author":"Wilson","year":"2002","journal-title":"Int. J. Aviat. Psychol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.apergo.2018.08.028","article-title":"Measuring mental workload using physiological measures: A systematic review","volume":"74","author":"Charles","year":"2019","journal-title":"Appl. Ergon."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/S0166-4115(08)62386-9","article-title":"Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research","volume":"Volume 52","author":"Hancock","year":"1988","journal-title":"Advances in Psychology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1177\/154193120605000909","article-title":"NASA-Task Load Index (NASA-TLX); 20 years later","volume":"50","author":"Hart","year":"2006","journal-title":"Proc. Hum. Factors Ergon. Soc. Annu. Meet."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1037\/0003-066X.54.2.93","article-title":"Self-reports: How the questions shape the answers","volume":"54","author":"Schwarz","year":"1999","journal-title":"Am. Psychol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1509\/jmkr.38.2.143.18840","article-title":"Response styles in marketing research: A cross-national investigation","volume":"38","author":"Baumgartner","year":"2001","journal-title":"J. Mark. Res."},{"key":"ref_8","unstructured":"Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93, 1043\u20131065."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/0301-0511(92)90016-N","article-title":"Measurement and analysis methods of heart rate and respiration for use in applied environments","volume":"34","author":"Mulder","year":"1992","journal-title":"Biol. Psychol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1177\/001872088702900204","article-title":"Operator effort and the measurement of heart-rate variability","volume":"29","author":"Aasman","year":"1987","journal-title":"Hum. Factors"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Boucsein, W. (2012). Electrodermal Activity, Springer. [2nd ed.].","DOI":"10.1007\/978-1-4614-1126-0"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Posada-Quintero, H.F., and Chon, K.H. (2020). Innovations in electrodermal activity data collection and signal processing: A systematic review. Sensors, 20.","DOI":"10.3390\/s20020479"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"520","DOI":"10.3109\/10253890.2013.807243","article-title":"The effect of stress on core and peripheral body temperature in humans","volume":"16","author":"Vinkers","year":"2013","journal-title":"Stress"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/0301-0511(95)05165-1","article-title":"Physiological indices of workload in a simulated flight task","volume":"42","author":"Veltman","year":"1996","journal-title":"Biol. Psychol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1518\/hfes.45.4.635.27088","article-title":"Real-time assessment of mental workload using psychophysiological measures and artificial neural networks","volume":"45","author":"Wilson","year":"2003","journal-title":"Hum. Factors"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.bbe.2019.12.002","article-title":"Classification of pilots\u2019 mental states using a multimodal deep learning network","volume":"40","author":"Han","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., and Van Laerhoven, K. (2018). Introducing WESAD, a multimodal dataset for wearable stress and affect detection. Proceedings of the 20th ACM International Conference on Multimodal Interaction (ICMI\u201918), Association for Computing Machinery.","DOI":"10.1145\/3242969.3242985"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"235","DOI":"10.30773\/pi.2017.08.17","article-title":"Stress and heart rate variability: A meta-analysis and review of the literature","volume":"15","author":"Kim","year":"2018","journal-title":"Psychiatry Investig."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITS.2005.848368","article-title":"Detecting stress during real-world driving tasks using physiological sensors","volume":"6","author":"Healey","year":"2005","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sano, A., and Picard, R.W. (2013). Stress recognition using wearable sensors and mobile phones. Proceedings of the 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), IEEE.","DOI":"10.1109\/ACII.2013.117"},{"key":"ref_21","unstructured":"Plarre, K., Raij, A., Hossain, S.M., Ali, A.A., Nakajima, M., Al\u2019Absi, M., Ertin, E., Kamarck, T., Kumar, S., and Scott, M. (2011). Continuous inference of psychological stress from sensory measurements collected in the natural environment. Proceedings of the 10th ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN), IEEE."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dehais, F., Dupr\u00e8s, A., Blum, S., Drougard, N., Scannella, S., Roy, R.N., and Lotte, F. (2019). Monitoring pilot\u2019s mental workload using ERPs and spectral power with a six-dry-electrode EEG system in real flight conditions. Sensors, 19.","DOI":"10.3390\/s19061324"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1038\/s41598-023-29647-0","article-title":"Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight","volume":"13","author":"Wilson","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Guo, D., Wang, C., Qin, Y., Shang, L., Gao, A., Tan, B., Zhou, Y., and Wang, G. (2025). Assessment of flight fatigue using heart rate variability and machine learning approaches. Front. Neurosci., 19.","DOI":"10.3389\/fnins.2025.1621638"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Masi, G., Amprimo, G., Ferraris, C., and Priano, L. (2023). Stress and workload assessment in aviation\u2014A narrative review. Sensors, 23.","DOI":"10.3390\/s23073556"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2242","DOI":"10.1080\/00140139.2023.2194592","article-title":"Integrating physiological monitoring systems in military aviation: A brief narrative review of its importance, opportunities, and risks","volume":"66","author":"Shaw","year":"2023","journal-title":"Ergonomics"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1007\/s00421-019-04142-5","article-title":"RR interval signal quality of a heart rate monitor and an ECG Holter at rest and during exercise","volume":"119","author":"Schweizer","year":"2019","journal-title":"Eur. J. Appl. Physiol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Schaffarczyk, M., Rogers, B., Reer, R., and Gronwald, T. (2022). Validity of the Polar H10 sensor for heart rate variability analysis during resting state and incremental exercise in recreational men and women. Sensors, 22.","DOI":"10.3390\/s22176536"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"gix019","DOI":"10.1093\/gigascience\/gix019","article-title":"The need to approximate the use-case in clinical machine learning","volume":"6","author":"Saeb","year":"2017","journal-title":"GigaScience"},{"key":"ref_30","first-page":"2079","article-title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"103589","DOI":"10.1016\/j.mex.2025.103589","article-title":"An in-flight multimodal data collection method for assessing pilot cognitive states and performance in general aviation","volume":"15","author":"Xu","year":"2025","journal-title":"MethodsX"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fung, H.W., Lam, S.K.K., and Chien, W.T. (2024). Reliability and mental health correlates of a single-item measure of self-rated mental health (SRMH) in the Chinese context. Healthcare, 12.","DOI":"10.3390\/healthcare12010122"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Castaldo, R., Montesinos, L., Melillo, P., James, C., and Pecchia, L. (2019). Ultra-short term HRV features as surrogates of short term HRV: A case study on mental stress detection in real life. BMC Med. Inform. Decis. Mak., 19.","DOI":"10.1186\/s12911-019-0742-y"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Shaffer, F., and Ginsberg, J.P. (2017). An overview of heart rate variability metrics and norms. Front. Public Health, 5.","DOI":"10.3389\/fpubh.2017.00258"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1089\/tmj.2014.0104","article-title":"Reliability of ultra-short-term analysis as a surrogate of standard 5-min analysis of heart rate variability","volume":"21","author":"Baek","year":"2015","journal-title":"Telemed. e-Health"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e18761","DOI":"10.2196\/18761","article-title":"A novel smartphone app for the measurement of ultra\u2013short-term and short-term heart rate variability: Validity and reliability study","volume":"8","author":"Chen","year":"2020","journal-title":"JMIR mHealth uHealth"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Taoum, A., Bisiaux, A., Tilquin, F., Le Guillou, Y., and Carrault, G. (2022). Validity of ultra-short-term HRV analysis using PPG\u2014A preliminary study. Sensors, 22.","DOI":"10.3390\/s22207995"},{"key":"ref_38","first-page":"647","article-title":"Decomposition of skin conductance data by means of nonnegative deconvolution","volume":"47","author":"Benedek","year":"2010","journal-title":"Psychophysiology"},{"key":"ref_39","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.Y. (2017, January 4\u20139). LightGBM: A highly efficient gradient boosting decision tree. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD\u201916), Association for Computing Machinery.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_44","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Qin, L., Chen, Y., Zhang, C., Liu, G., and Wen, W. (2025, January 17\u201319). Real-Time Stress Recognition for Pilots in Naturalistic Scenarios Using PPI Signals and Hybrid SVM-LSTM Models. Proceedings of the 2025 6th International Conference on Machine Learning and Computer Application (ICMLCA), Shenzhen, China.","DOI":"10.1109\/ICMLCA66850.2025.11336781"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: A practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. Ser. (Methodol.)"},{"key":"ref_47","unstructured":"Lundberg, S.M., and Lee, S.I. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Haseeb, M., Nadeem, R., Sultana, N., Naseer, N., Nazeer, H., and Dehais, F. (2025). Monitoring pilots\u2019 mental workload in real flight conditions using multinomial logistic regression with a ridge estimator. Front. Robot. AI, 12.","DOI":"10.3389\/frobt.2025.1441801"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Gateau, T., Durantin, G., Lancelot, F., Scannella, S., and Dehais, F. (2015). Real-time state estimation in a flight simulator using fNIRS. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0121279"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ghosh Hajra, S., Xi, P., and Law, A. (2020). A comparison of ECG and EEG metrics for in-flight monitoring of helicopter pilot workload. Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE.","DOI":"10.1109\/SMC42975.2020.9283499"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"103623","DOI":"10.1016\/j.bspc.2022.103623","article-title":"Machine learning-based approach for identifying mental workload of pilots","volume":"75","author":"Mohanavelu","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Vindigni, C.R., Iacolino, G., Esposito, A., Orlando, C., and Alaimo, A. (2026). Decision tree-based pilot workload prediction through optimized HRV features selection. Aerospace, 13.","DOI":"10.3390\/aerospace13010073"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"e70019","DOI":"10.1002\/hfm.70019","article-title":"A psychophysiological model based on machine learning algorithms for evaluating commercial airline pilots\u2019 mental workload in flight-simulation context","volume":"35","author":"Wang","year":"2025","journal-title":"Hum. Factors Ergon. Manuf. Serv. Ind."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"11774","DOI":"10.1109\/JSEN.2023.3247341","article-title":"Pilot stress detection through physiological signals using a transformer-based deep learning model","volume":"23","author":"Li","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"834","DOI":"10.3357\/AMHP.4782.2017","article-title":"Psychophysiological assessment in pilots performing challenging simulated and real flight maneuvers","volume":"88","author":"Johannes","year":"2017","journal-title":"Aerosp. Med. Hum. Perform."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, P., Houghton, R., and Majumdar, A. (2024). Detecting and predicting pilot mental workload using heart rate variability: A systematic review. Sensors, 24.","DOI":"10.3390\/s24123723"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1177\/0018720812448394","article-title":"Do team and individual debriefs enhance performance? A meta-analysis","volume":"55","author":"Tannenbaum","year":"2013","journal-title":"Hum. Factors"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1027\/2192-0923\/a000114","article-title":"A review of debriefing practices: Toward a framework for airline pilot debriefing","volume":"7","author":"Kikkawa","year":"2017","journal-title":"Aviat. Psychol. Appl. Hum. Factors"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"385","DOI":"10.2307\/2136404","article-title":"A global measure of perceived stress","volume":"24","author":"Cohen","year":"1983","journal-title":"J. Health Soc. Behav."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3627\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T04:49:11Z","timestamp":1780980551000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3627"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,6]]},"references-count":59,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26123627"],"URL":"https:\/\/doi.org\/10.3390\/s26123627","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,6]]}}}