{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T09:50:18Z","timestamp":1785577818786,"version":"3.56.0"},"reference-count":90,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T00:00:00Z","timestamp":1645747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>Artificial Intelligence (AI) has broadly connected the medical field at various levels of diagnosis based on the congruous data generated. Different types of bio-signal can be used to monitor a patient\u2019s condition and in decision making. Medical equipment uses signals to communicate information to care staff. AI algorithms and approaches will help to predict health problems and check the health status of organs, while AI prediction, classification, and regression algorithms are helping the medical industry to protect from health hazards. The early prediction and detection of health conditions will guide people to stay healthy. This paper represents the scope of bio-signals using AI in the medical area. It will illustrate possible case studies relevant to bio-signals generated through IoT sensors. The bio-signals that retrospectively occur are discussed, and the new challenges of medical diagnosis using bio-signals are identified.<\/jats:p>","DOI":"10.3390\/jsan11010017","type":"journal-article","created":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T20:47:17Z","timestamp":1645994837000},"page":"17","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Bio-Signals in Medical Applications and Challenges Using Artificial Intelligence"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2816-6857","authenticated-orcid":false,"given":"Mudrakola","family":"Swapna","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Matrusri Engineering College, Hyderabad 500058, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8258-9987","authenticated-orcid":false,"given":"Uma Maheswari","family":"Viswanadhula","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad 501218, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8508-6066","authenticated-orcid":false,"given":"Rajanikanth","family":"Aluvalu","sequence":"additional","affiliation":[{"name":"Department of IT, Chaitanya Bharathi Institute of Technology(A), Hyderabad 500075, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3752-7220","authenticated-orcid":false,"given":"Vijayakumar","family":"Vardharajan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, The University of New South Wales, Sydney 1466, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2653-3780","authenticated-orcid":false,"given":"Ketan","family":"Kotecha","sequence":"additional","affiliation":[{"name":"Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International University, Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1287\/opre.6.1.1","article-title":"Heuristic problem solving: The next advance in operations research","volume":"6","author":"Simon","year":"1958","journal-title":"Oper. Res."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"McCarthy, J. (1989). Artificial intelligence, logic and formalizing common sense. Philosophical Logic and Artificial Intelligence, Springer.","DOI":"10.1007\/978-94-009-2448-2_6"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jackson, P.C. (2019). Introduction to Artificial Intelligence, Courier Dover Publications.","DOI":"10.18356\/d94175df-en"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"02028","DOI":"10.1051\/e3sconf\/201911002028","article-title":"Opportunities and challenges of artificial intelligence in healthcare","volume":"110","author":"Iliashenko","year":"2019","journal-title":"E3S Web Conf."},{"key":"ref_5","unstructured":"Chen, C.H. (1988). Signal Processing Handbook, CRC Press."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pillai, S., Upadhyay, A., Sayson, D., Nguyen, B.H., and Tran, S.D. (2022). Advances in Medical Wearable Biosensors: Design, Fabrication and Materials Strategies in Healthcare Monitoring. Molecules, 27.","DOI":"10.3390\/molecules27010165"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Harikrishna, E., and Reddy, K.A. (2021). Use of Transforms in Biomedical Signal Processing and Analysis. Real Perspective of Fourier Transforms and Current Developments in Superconductivity, IntechOpen.","DOI":"10.5772\/intechopen.98239"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"275","DOI":"10.4097\/kja.19475","article-title":"Discovering hidden information in biosignals from patients using artificial intelligence","volume":"73","author":"Yoon","year":"2020","journal-title":"Korean J. Anesthesiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1515\/bmte.2001.46.5.129","article-title":"Artificial intelligence in sleep analysis (ARTISANA)--modelling visual processes in sleep classification","volume":"46","author":"Schwaibold","year":"2001","journal-title":"Biomed. Technik. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, F., Park, C., Tham, Y.J., Tsai, T.Y., Dabbish, L., Kaufman, G., and Monroy-Hern\u00e1ndez, A. (2021). Significant Otter: Understanding the Role of Biosignals in Communication. arXiv.","DOI":"10.1145\/3411764.3445200"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., and Aljaaf, A.J. (2020). A systematic review on supervised and unsupervised machine learning algorithms for data science. Supervised Unsupervised Learn. Data Sci., 3\u201321.","DOI":"10.1007\/978-3-030-22475-2_1"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12936-020-03530-z","article-title":"A timed tally counter for microscopic examination of thick blood smears in malaria studies","volume":"20","author":"Nuel","year":"2021","journal-title":"Malar. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"S93","DOI":"10.3349\/ymj.2022.63.S93","article-title":"Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis","volume":"63","author":"Lee","year":"2022","journal-title":"Yonsei Med. J."},{"key":"ref_14","first-page":"110","article-title":"Lesions Detection of Multiple Sclerosis in 3D Brian MR Images by Using Artificial Immune Systems and Support Vector Machines","volume":"15","author":"Merzoug","year":"2021","journal-title":"Int. J. Cogn. Inform. Nat. Intell. (IJCINI)"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Moraru, L., Moldovanu, S., and Biswas, A. (2016). Intensity-Based Classification and Related Methods in Brain MR Images. Classification and Clustering in Biomedical Signal Processing, Hershey.","DOI":"10.4018\/978-1-5225-0571-6.ch022"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"20161142","DOI":"10.1587\/elex.14.20161142","article-title":"Low-power low-data-loss bio-signal acquisition system for intelligent electrocardiogram detection","volume":"14","author":"Wang","year":"2017","journal-title":"IEICE Electron. Express"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"060005","DOI":"10.1063\/5.0070810","article-title":"Vital signs measurements & development for e-health care application","volume":"2385","author":"Shamini","year":"2022","journal-title":"AIP Conf. Proc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103513","DOI":"10.1109\/ACCESS.2021.3097751","article-title":"One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments","volume":"9","author":"Cheikhrouhou","year":"2021","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e018656","DOI":"10.1161\/JAHA.120.018656","article-title":"Vascular Aging Detected by Peripheral Endothelial Dysfunction Is Associated with ECG-Derived Physiological Aging","volume":"10","author":"Toya","year":"2021","journal-title":"J. Am. Heart Assoc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"031001","DOI":"10.1088\/1741-2552\/ab0ab5","article-title":"Deep learning for electroencephalogram (EEG) classification tasks: A review","volume":"16","author":"Craik","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103417","DOI":"10.1016\/j.bspc.2021.103417","article-title":"Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies","volume":"73","author":"Shoeibi","year":"2022","journal-title":"Biomed. Signal Proc. Control."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Georgieva, O., Milanov, S., and Georgieva, P. (2013, January 19\u201321). Cluster analysis for EEG biosignal discrimination. Proceedings of the 2013 IEEE INISTA, Albena, Bulgaria.","DOI":"10.1109\/INISTA.2013.6577646"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.ijid.2020.11.146","article-title":"Nerve conduction study and electromyography findings in patients recovering from COVID-19\u2013Case report","volume":"103","author":"Daia","year":"2021","journal-title":"Int. J. Infect. Dis."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/RBME.2021.3078190","article-title":"Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey","volume":"15","author":"Jiang","year":"2022","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100056","DOI":"10.1016\/j.medntd.2020.100056","article-title":"Developing a Low-cost, smart, handheld electromyography biofeedback system for telerehabilitation with Clinical Evaluation","volume":"10","author":"Yassin","year":"2021","journal-title":"Med. Nov. Technol. Devices"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ashby, C., Bhatia, A., Tenore, F., and Vogelstein, J. (May, January 27). Low-cost electroencephalogram (EEG) based authentication. Proceedings of the 2011 5th International IEEE\/EMBS Conference on Neural Engineering, Cancun, Mexico.","DOI":"10.1109\/NER.2011.5910581"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8767865","DOI":"10.1155\/2020\/8767865","article-title":"Using AI-based classification techniques to process EEG data collected during the visual short-term memory assessment","volume":"2020","author":"Antonijevic","year":"2020","journal-title":"J. Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"282","DOI":"10.3340\/jkns.2020.0179","article-title":"Triggered Electrooculography for Identification of Oculomotor and Abducens Nerves during Skull Base Surgery","volume":"64","author":"Jeong","year":"2021","journal-title":"J. Korean Neurosurg. Soc."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rakhmatulin, I., and Volkl, S. (2022). PIEEG: Turn a Raspberry Pi into a Brain-Computer-Interface to measure biosignals. arXiv.","DOI":"10.2139\/ssrn.4005639"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.bja.2021.03.011","article-title":"VitalDB: Fostering collaboration in anaesthesia research","volume":"127","author":"Vistisen","year":"2021","journal-title":"Br. J. Anaesth"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jang, J.H., Kim, T.Y., Lim, H.S., and Yoon, D. (2021). Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0260612"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"103789","DOI":"10.1016\/j.jbi.2021.103789","article-title":"Critical carE Database for Advanced Research (CEDAR): An automated method to support intensive care units with electronic health record data","volume":"118","author":"Schenck","year":"2021","journal-title":"J. Biomed. Inform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"19","DOI":"10.4258\/hir.2021.27.1.19","article-title":"Effectiveness of Transfer Learning for Deep Learning-Based Electrocardiogram Analysis","volume":"27","author":"Jang","year":"2021","journal-title":"Healthc. Inform. Res."},{"key":"ref_34","unstructured":"Schuller, B., Friedmann, F., and Eyben, F. (2014, January 26\u201331). The Munich Biovoice Corpus: Effects of physical exercising, heart rate, and skin conductance on human speech production. Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC\u201914), Reykjavik, Iceland."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lee, J., Yang, S., Lee, S., and Kim, H.C. (2019). Analysis of pulse arrival time as an indicator of blood pressure in a large surgical biosignal database: Recommendations for developing ubiquitous blood pressure monitoring methods. J. Clin. Med., 8.","DOI":"10.3390\/jcm8111773"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"012012","DOI":"10.1088\/1757-899X\/1042\/1\/012012","article-title":"A Multifarious Diagnosis of Breast Cancer Using Mammogram Images\u2013Systematic Review","volume":"Volume 1042","author":"Swapna","year":"2021","journal-title":"Proceedings of the IOP Conference Series: Materials Science and Engineering"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1126\/science.abg1834","article-title":"Beware explanations from AI in health care","volume":"373","author":"Babic","year":"2021","journal-title":"Science"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"17","DOI":"10.14419\/ijet.v7i4.6.20225","article-title":"Local Directional Threshold based Binary Patterns for Facial Expression Recognition and Analysis","volume":"7","author":"Maheswari","year":"2018","journal-title":"Int. J. Eng. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4775","DOI":"10.1007\/s12652-020-01886-3","article-title":"Local directional maximum edge patterns for facial expression recognition","volume":"12","author":"Maheswari","year":"2020","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_40","first-page":"1","article-title":"A survey on local textural patterns for facial feature extraction","volume":"8","author":"Prasad","year":"2018","journal-title":"Int. J. Comput. Vis. Image Process. (IJCVIP)"},{"key":"ref_41","first-page":"37","article-title":"Is Naive Bayes a good classifier for document classification","volume":"5","author":"Ting","year":"2011","journal-title":"Int. J. Softw. Eng. Its Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"012075","DOI":"10.1088\/1742-6596\/1732\/1\/012075","article-title":"January. Comparative Study on Defects and Faults Detection of Main Transformer Based on Logistic Regression and Naive Bayes Algorithm","volume":"1732","author":"Hu","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_43","first-page":"9","article-title":"Random forest vs logistic regression: Binary classification for heterogeneous datasets","volume":"1","author":"Kirasich","year":"2018","journal-title":"SMU Data Sci. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Guarracino, M.R., and Nebbia, A. (2009). Predicting protein-protein interactions with k-nearest neighbors classification algorithm. International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Springer.","DOI":"10.1007\/978-3-642-14571-1_10"},{"key":"ref_45","first-page":"213","article-title":"Decision trees for binary classification variables grow equally with the Gini impurity measure and Pearson\u2019s chi-square test","volume":"2","author":"Grabmeier","year":"2007","journal-title":"Int. J. Bus. Intell. Data Min."},{"key":"ref_46","unstructured":"Tang, Y., Jin, B., Sun, Y., and Zhang, Y.Q. (2004, January 7\u20138). Granular support vector machines for medical binary classification problems. Proceedings of the 2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology, La Jolla, CA, USA."},{"key":"ref_47","first-page":"1265","article-title":"E-Mail Spam Filtering","volume":"9","author":"Upadhyay","year":"2021","journal-title":"Int. J. Res."},{"key":"ref_48","unstructured":"Zhang, L., Jack, L.B., and Nandi, A.K. (2005, January 23\u201323). Extending genetic programming for multi-class classification by combining k-nearest neighbor. Proceedings of the (ICASSP\u201905) IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, PA, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"105922","DOI":"10.1016\/j.knosys.2020.105922","article-title":"A hybrid scheme-based one-vs-all decision trees for multi-class classification tasks","volume":"198","author":"Yan","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_50","unstructured":"Rennie, J.D. (2021, December 28). Improving Multi-Class Text Classification with Naive Bayes. Available online: https:\/\/www.researchgate.net\/publication\/279812722_Improving_Multi-class_Text_Classification_with_Naive_Bayes."},{"key":"ref_51","first-page":"215","article-title":"An improved random forest classifier for multi-class classification","volume":"3","author":"Chaudhary","year":"2016","journal-title":"Inf. Process. Agric."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Li, P. (2009, January 14\u201318). Abc-boost: Adaptive base class boost for multi-class classification. Proceedings of the 26th Annual international conference on Machine Learning 2009, Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553455"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"De Comit\u00e9, F., Gilleron, R., and Tommasi, M. (2003). Learning multi-label alternating decision trees from texts and data. International Workshop on Machine Learning and Data Mining in Pattern Recognition, Springer.","DOI":"10.1007\/3-540-45065-3_4"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Joly, A., Geurts, P., and Wehenkel, L. (2014). Random forests with random projections of the output space for high dimensional multi-label classification. Joint European Conference on Machine Learning and Knowledge Discovery in Database, Springer.","DOI":"10.1007\/978-3-662-44848-9_39"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Rapp, M., Menc\u00eda, E.L., F\u00fcrnkranz, J., Nguyen, V.L., and H\u00fcllermeier, E. (2020). Learning gradient boosted multi-label classification rules. arXiv.","DOI":"10.1007\/978-3-030-67664-3_8"},{"key":"ref_56","first-page":"130","article-title":"Decision tree methods: Applications for classification and prediction","volume":"27","author":"Song","year":"2015","journal-title":"Shanghai Arch. Psychiatry"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/BF01000407","article-title":"The importance of attribute selection measures in decision tree induction","volume":"15","author":"Liu","year":"1994","journal-title":"Mach. Learn."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Valecha, H., Varma, A., Khare, I., Sachdeva, A., and Goyal, M. (2018, January 2\u20134). Prediction of consumer behaviour using random forest algorithm. Proceedings of the 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Gorakhpur, India.","DOI":"10.1109\/UPCON.2018.8597070"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Dai, B., Chen, R.C., Zhu, S.Z., and Zhang, W.W. (2018, January 6). Using random forest algorithm for breast cancer diagnosis. Proceedings of the 2018 International Symposium on Computer, Consumer and Control (IS3C), Taichung, Taiwan.","DOI":"10.1109\/IS3C.2018.00119"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"18605","DOI":"10.1007\/s11042-016-4215-3","article-title":"SVM based robust watermarking for enhanced medical image security","volume":"76","author":"Rai","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"104696","DOI":"10.1016\/j.compbiomed.2021.104696","article-title":"Recognition of human emotions using EEG signals: A review","volume":"136","author":"Rahman","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"105122","DOI":"10.1016\/j.cmpb.2019.105122","article-title":"R-Ensembler: A greedy rough set based ensemble attribute selection algorithm with kNN imputation for classification of medical data","volume":"184","author":"Bania","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.neucom.2015.08.112","article-title":"Efficient kNN classification algorithm for big data","volume":"195","author":"Deng","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Pripp, A.H., and Stani\u0161i\u0107, M. (2017). Association between biomarkers and clinical characteristics in chronic subdural hematoma patients assessed with lasso regression. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0186838"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"7110","DOI":"10.1016\/j.eswa.2015.04.066","article-title":"Predictive modeling of hospital readmissions using metaheuristics and data mining","volume":"42","author":"Zheng","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_66","first-page":"295","article-title":"A multilevel, multivariate model for studying school climate with estimation via the EM algorithm and application to US high-school data","volume":"16","author":"Raudenbush","year":"1991","journal-title":"J. Educ. Stat."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s40201-018-00324-z","article-title":"Comparative study of predicting hospital solid waste generation using multiple linear regression and artificial intelligence","volume":"17","author":"Golbaz","year":"2019","journal-title":"J. Environ. Health Sci. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.neunet.2009.07.001","article-title":"Sparse kernel learning with LASSO and Bayesian inference algorithm","volume":"23","author":"Gao","year":"2010","journal-title":"Neural Netw."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Chen, Q., and Deng, M. (2021). Study of a Privacy Preserving Logistic Regression Algorithm (PPLRA) for Data Privacy in the Context of Big Data, IOP Publishing.","DOI":"10.1088\/1742-6596\/2083\/3\/032059"},{"key":"ref_70","unstructured":"Liu, H., Wang, L., and Zhao, T. (2014). Multivariate regression with calibration. Advances in Neural Information Processing Systems 27, MIT Press."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Kokubun, K. (2022). Factors That Attract the Population: Empirical Research by Multiple Regression Analysis Using Data by Prefecture in Japan. Sustainability, 14.","DOI":"10.3390\/su14031595"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Farhadi, S., Salehi, M., Moieni, A., Safaie, N., and Sabet, M.S. (2020). Modeling of paclitaxel biosynthesis elicitation in Corylus avellana cell culture using adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) and multiple regression methods. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0237478"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2387","DOI":"10.1016\/j.aej.2017.09.011","article-title":"Brain tumour detection using mean shift clustering and GLCM features with edge adaptive total variation denoising technique","volume":"57","author":"Vallabhaneni","year":"2018","journal-title":"Alex. Eng. J."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Khan, M.M.R., Siddique, M.A.B., Arif, R.B., and Oishe, M.R. (2018, January 13\u201315). ADBSCAN: Adaptive density-based spatial clustering of applications with noise for identifying clusters with varying densities. Proceedings of the 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), Dhaka, Bangladesh.","DOI":"10.1109\/CEEICT.2018.8628138"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1109\/JBHI.2013.2287504","article-title":"Designing a robust activity recognition framework for health and exergaming using wearable sensors","volume":"18","author":"Alshurafa","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s10115-006-0027-5","article-title":"Fast agglomerative hierarchical clustering algorithm using Locality-Sensitive Hashing","volume":"12","author":"Koga","year":"2007","journal-title":"Knowl. Inf. Syst."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.1109\/TASLP.2017.2752365","article-title":"Biosignal-based spoken communication: A survey","volume":"25","author":"Schultz","year":"2017","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Processing"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1037\/0022-3514.37.9.1519","article-title":"The role of facial response in the experience of emotion","volume":"37","author":"Tourangeau","year":"1979","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_79","first-page":"1","article-title":"Applications using earphone with biosignal sensors","volume":"12","author":"Sano","year":"2010","journal-title":"Hum. Interface Soc. Meet."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Van Den Broek, E.L., Lis\u00fd, V., Janssen, J.H., Westerink, J.H., Schut, M.H., and Tuinenbreijer, K. (2009). Affective man-machine interface: Unveiling human emotions through biosignals. International Joint Conference on Biomedical Engineering Systems and Technologies, Springer.","DOI":"10.1007\/978-3-642-11721-3_2"},{"key":"ref_81","unstructured":"Suh, Y.A., Kim, J.H., and Yim, M.S. (2018, January 25\u201328). Proposing A Worker\u2019s Mental Health Assessment Using Bio-Signals. Proceedings of the 3rd International Conference on Human Resource Development for Nuclear Power Programmes: Meeting Challenges to Ensure the Future Nuclear Workforce Capability, Gyeongju, Korea."},{"key":"ref_82","first-page":"114","article-title":"Estimating biosignals using the human voice","volume":"350","author":"Coutinho","year":"2015","journal-title":"Science"},{"key":"ref_83","first-page":"4977620","article-title":"Eye movement prediction based on adaptive BP neural network","volume":"2021","author":"Tang","year":"2021","journal-title":"Sci. Program."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Yamashita, K., Izumi, S., Nakano, M., Fujii, T., Konishi, T., Kawaguchi, H., Kimura, H., Marumoto, K., Fuchikami, T., and Fujimori, Y. (2013, January 16\u201319). A 38 \u03bcA wearable biosignal monitoring system with near field communication. Proceedings of the 2013 IEEE 11th International New Circuits and Systems Conference (NEWCAS), Paris, France.","DOI":"10.1109\/NEWCAS.2013.6573637"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Islam, M.Z., Hossain, M.S., ul Islam, R., and Andersson, K. (June, January 30). Static hand gesture recognition using convolutional neural network with data augmentation. Proceedings of the 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Spokane, WA, USA.","DOI":"10.1109\/ICIEV.2019.8858563"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Xie, B., Meng, J., Li, B., and Harland, A. (2020, January 27\u201329). Gesture recognition from bio-signals using hybrid deep neural networks. Proceedings of the 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China.","DOI":"10.1109\/ICAICA50127.2020.9182510"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Asif, A.R., Waris, A., Gilani, S.O., Jamil, M., Ashraf, H., Shafique, M., and Niazi, I.K. (2020). Performance evaluation of convolutional neural network for hand gesture recognition using EMG. Sensors, 20.","DOI":"10.3390\/s20061642"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TSMCA.2008.918624","article-title":"Toward emotion recognition in car-racing drivers: A biosignal processing approach","volume":"38","author":"Katsis","year":"2008","journal-title":"IEEE Trans. Syst. Man Cybern.-Part A Syst. Hum."},{"key":"ref_89","first-page":"241","article-title":"Frequency study of facial electromyography signals with respect to emotion recognition","volume":"59","author":"Selvaraj","year":"2014","journal-title":"Biomed. Eng. \/Biomed. Tech."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Nie, J., Hu, Y., Wang, Y., Xia, S., and Jiang, X. (2020, January 21\u201324). SPIDERS: Low-cost wireless glasses for continuous in-situ bio-signal acquisition and emotion recognition. Proceedings of the 2020 IEEE\/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI), Sydney, Australia.","DOI":"10.1109\/IoTDI49375.2020.00011"}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/11\/1\/17\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:27:41Z","timestamp":1760135261000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/11\/1\/17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,25]]},"references-count":90,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["jsan11010017"],"URL":"https:\/\/doi.org\/10.3390\/jsan11010017","relation":{},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,25]]}}}