{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T00:08:15Z","timestamp":1793146095595,"version":"4.5.0"},"reference-count":94,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"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>This study focuses on the diagnostic analysis of cartilage damage in the knee joint based on acoustic signals generated by the joint. The research utilizes a combination of advanced signal processing techniques, specifically empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA), alongside convolutional neural networks (CNNs) for classification and detection tasks. Acoustic signals, often reflecting the mechanical behavior of the joint during movement, serve as a non-invasive diagnostic tool for assessing the cartilage condition. EEMD is applied to decompose the signals into intrinsic mode functions (IMFs), which are then analyzed using DFA to quantify the scaling properties and detect irregularities indicative of cartilage damage. The separation of individual frequency components allows for multi-scale analysis of the signals, with each of the functions resulting from the analysis reflecting local variations in the amplitude and frequency over time and allowing for effective removal of noise present in the signal. The CNN model is trained on features extracted from these signals to accurately classify different stages of cartilage degeneration. The proposed method demonstrates the potential for early detection of knee joint pathology, providing a valuable tool for preventive healthcare and reducing the need for invasive diagnostic procedures. The results suggest that the combination of EEMD-DFA for feature extraction and CNN for classification offers a promising approach for the non-invasive assessment of cartilage damage.<\/jats:p>","DOI":"10.3390\/s25030706","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T08:48:23Z","timestamp":1737708503000},"page":"706","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Multi-Scale Analysis of Knee Joint Acoustic Signals for Cartilage Degeneration Assessment"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3289-2421","authenticated-orcid":false,"given":"Anna","family":"Machrowska","sequence":"first","affiliation":[{"name":"Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4063-8503","authenticated-orcid":false,"given":"Robert","family":"Karpi\u0144ski","sequence":"additional","affiliation":[{"name":"Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9116-5481","authenticated-orcid":false,"given":"Marcin","family":"Maciejewski","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information Technology, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4658-4569","authenticated-orcid":false,"given":"J\u00f3zef","family":"Jonak","sequence":"additional","affiliation":[{"name":"Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7137-7145","authenticated-orcid":false,"given":"Przemys\u0142aw","family":"Krakowski","sequence":"additional","affiliation":[{"name":"Department of Trauma Surgery and Emergency Medicine, Medical University of Lublin, Staszica 11, 20-081 Lublin, Poland"},{"name":"Orthopaedic and Sports Traumatology Department, Carolina Medical Center, Pory 78, 02-757 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3846-835X","authenticated-orcid":false,"given":"Arkadiusz","family":"Syta","sequence":"additional","affiliation":[{"name":"Department of Technical Computer Science, Faculty of Mathematics and Technical Computer Science, Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Krakowski, P., Rejniak, A., Sobczyk, J., and Karpi\u0144ski, R. (2024). Cartilage Integrity: A Review of Mechanical and Frictional Properties and Repair Approaches in Osteoarthritis. Healthcare, 12.","DOI":"10.3390\/healthcare12161648"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Link, J.M., Salinas, E.Y., Hu, J.C., and Athanasiou, K.A. (2020). The Tribology of Cartilage: Mechanisms, Experimental Techniques, and Relevance to Translational Tissue Engineering. Clin. Biomech., 79.","DOI":"10.1016\/j.clinbiomech.2019.10.016"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.joca.2014.09.021","article-title":"Tribological and Material Properties for Cartilage of and throughout the Bovine Stifle: Support for the Altered Joint Kinematics Hypothesis of Osteoarthritis","volume":"23","author":"Moore","year":"2015","journal-title":"Osteoarthr. Cartil."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"149","DOI":"10.3109\/03008208909043895","article-title":"Some Biochemical and Biophysical Parameters for the Study of the Pathogenesis of Osteoarthritis: A Comparison Between the Processes of Ageing and Degeneration in Human Hip Cartilage","volume":"19","author":"Grushko","year":"1989","journal-title":"Connect. Tissue Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.joca.2011.02.008","article-title":"Natural History of Cartilage Damage and Osteoarthritis Progression on Magnetic Resonance Imaging in a Population-Based Cohort with Knee Pain","volume":"19","author":"Cibere","year":"2011","journal-title":"Osteoarthr. Cartil."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1007\/s10237-006-0029-y","article-title":"Considerations on Joint and Articular Cartilage Mechanics","volume":"5","author":"Herzog","year":"2006","journal-title":"Biomech. Model. Mechanobiol."},{"key":"ref_7","first-page":"66","article-title":"Biomechanics of Cartilage","volume":"2","author":"Mansour","year":"2003","journal-title":"Kinesiol. Mech. Pathomech. Hum. Mov."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1146\/annurev.bioeng.2.1.691","article-title":"Cartilage Tissue Remodeling in Response to Mechanical Forces","volume":"2","author":"Grodzinsky","year":"2000","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3368","DOI":"10.1016\/j.arth.2017.05.052","article-title":"Total Knee Replacement in Young Patients: Survival and Causes of Revision in a Registry Population","volume":"32","author":"Castagnini","year":"2017","journal-title":"J. Arthroplast."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1136\/ard.16.4.494","article-title":"Radiological Assessment of Osteo-Arthrosis","volume":"16","author":"Kellgren","year":"1957","journal-title":"Ann. Rheum. Dis."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Krakowski, P., Karpi\u0144ski, R., Jojczuk, M., Nogalska, A., and Jonak, J. (2021). Knee MRI Underestimates the Grade of Cartilage Lesions. Appl. Sci., 11.","DOI":"10.3390\/app11041552"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1016\/j.joca.2014.06.020","article-title":"An Ultrasound Score for Knee Osteoarthritis: A Cross-Sectional Validation Study","volume":"22","author":"Riecke","year":"2014","journal-title":"Osteoarthr. Cartil."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Karpi\u0144ski, R., Prus, A., Jonak, K., and Krakowski, P. (2025). Vibroarthrography as a Noninvasive Screening Method for Early Diagnosis of Knee Osteoarthritis: A Review of Current Research. Appl. Sci., 15.","DOI":"10.3390\/app15010279"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Choi, S., Seo, S., Shin, B., Byun, H., Kersner, M., Kim, B., Kim, D., and Ha, S. (2019). Temporal Convolution for Real-Time Keyword Spotting on Mobile Devices. arXiv.","DOI":"10.21437\/Interspeech.2019-1363"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Triki, N., Karray, M., and Ksantini, M. (2023). A Real-Time Traffic Sign Recognition Method Using a New Attention-Based Deep Convolutional Neural Network for Smart Vehicles. Appl. Sci., 13.","DOI":"10.3390\/app13084793"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"110070","DOI":"10.1016\/j.knosys.2022.110070","article-title":"A Real-Time Adaptive Model for Bearing Fault Classification and Remaining Useful Life Estimation Using Deep Neural Network","volume":"259","author":"Gupta","year":"2023","journal-title":"Knowl. Based Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hasan, M.D.A., Balasubadra, K., Vadivel, G., Arunfred, N., Ishwarya, M.V., and Murugan, S. (2024, January 8\u201310). IoT-Driven Image Recognition for Microplastic Analysis in Water Systems Using Convolutional Neural Networks. Proceedings of the 2024 2nd International Conference on Computer, Communication and Control (IC4), Indore, India.","DOI":"10.1109\/IC457434.2024.10486490"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1016\/j.procs.2023.01.054","article-title":"Face Detection and Recognition Using Face Mesh and Deep Neural Network","volume":"218","author":"Hangaragi","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1111\/mice.12830","article-title":"A Deep Neural Network Framework for Real-time On-site Estimation of Acceleration Response Spectra of Seismic Ground Motions","volume":"38","author":"Fayaz","year":"2023","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"34","DOI":"10.35784\/acs-2019-19","article-title":"Diagnostic Factors for Opened and Closed Kinematic Chain of Vibroarthrography Signals","volume":"15","author":"Machrowska","year":"2019","journal-title":"Appl. Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"90","DOI":"10.35784\/acs-2024-18","article-title":"Application of Eemd-Dfa Algorithms and Ann Classification for Detection of Knee Osteoarthritis Using Vibroarthrography","volume":"20","author":"Machrowska","year":"2024","journal-title":"Appl. Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Karpi\u0144ski, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., and Nogalski, A. (2022). Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN\u2014Part I: Femoral-Tibial Joint. Sensors, 22.","DOI":"10.3390\/s22062176"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Karpi\u0144ski, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., and Nogalski, A. (2022). Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN\u2014Part II: Patellofemoral Joint. Sensors, 22.","DOI":"10.3390\/s22103765"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"012009","DOI":"10.1088\/1742-6596\/2130\/1\/012009","article-title":"Estimation of Differences in Selected Indices of Vibroacoustic Signals between Healthy and Osteoarthritic Patellofemoral Joints as a Potential Non-Invasive Diagnostic Tool","volume":"2130","author":"Krakowski","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Karpi\u0144ski, R., Szabelski, J., and Maksymiuk, J. (2019). Effect of Physiological Fluids Contamination on Selected Mechanical Properties of Acrylate Bone Cement. Materials, 12.","DOI":"10.3390\/ma12233963"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"136","DOI":"10.35784\/acs-2023-40","article-title":"Comparison of Selected Classification Methods Based on Machine Learning as a Diagnostic Tool for Knee Joint Cartilage Damage Based on Generated Vibroacoustic Processes","volume":"19","author":"Krakowski","year":"2023","journal-title":"Appl. Comput. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Petrillo, L., Martinelli, F., Santone, A., and Mercaldo, F. (2025). Explainable Security Requirements Classification Through Transformer Models. Future Internet, 17.","DOI":"10.3390\/fi17010015"},{"key":"ref_28","first-page":"1","article-title":"FDTs: A Feature Disentangled Transformer for Interpretable Squamous Cell Carcinoma Grading","volume":"12","author":"Huang","year":"2024","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3557","DOI":"10.1109\/JBHI.2024.3373438","article-title":"LA-ViT: A Network With Transformers Constrained by Learned-Parameter-Free Attention for Interpretable Grading in a New Laryngeal Histopathology Image Dataset","volume":"28","author":"Huang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102333","DOI":"10.1016\/j.inffus.2024.102333","article-title":"MamlFormer: Priori-Experience Guiding Transformer Network via Manifold Adversarial Multi-Modal Learning for Laryngeal Histopathological Grading","volume":"108","author":"Huang","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"17","DOI":"10.35784\/iapgos.6139","article-title":"Concept and validation of a system for recording vibroacoustic signals of the knee joint","volume":"14","author":"Machrowska","year":"2024","journal-title":"IAPGOS"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"36","DOI":"10.35784\/acs-2019-03","article-title":"Application of acoustic signal processing methods in detecting differences between open and closed kinematic chain movement for the knee joint","volume":"15","author":"Machrowska","year":"2019","journal-title":"Appl. Comput. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"012037","DOI":"10.1088\/1757-899X\/710\/1\/012037","article-title":"A Preliminary Study on the Use of EEMD-RQA Algorithms in the Detection of Degenerative Changes in Knee Joints","volume":"710","author":"Jonak","year":"2019","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"012010","DOI":"10.1088\/1742-6596\/2130\/1\/012010","article-title":"Analysis of Differences in Vibroacoustic Signals between Healthy and Osteoarthritic Knees Using EMD Algorithm and Statistical Analysis","volume":"2130","author":"Krakowski","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"012077","DOI":"10.1088\/1742-6596\/1706\/1\/012077","article-title":"Adaptive Denoising of ECG Using EMD, EEMD and CEEMDAN Signal Processing Techniques","volume":"1706","author":"Teja","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, X., and Sun, Y. (2021, January 23\u201326). A Method to Denoise the Epileptic EEG by EEMD and TFPF. Proceedings of the 2021 International Conference on Electronic Information Engineering and Computer Science (EIECS), Changchun, China.","DOI":"10.1109\/EIECS53707.2021.9587941"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4077","DOI":"10.1007\/s00170-020-05315-9","article-title":"Bearing Fault Diagnostics Using EEMD Processing and Convolutional Neural Network Methods","volume":"107","author":"Amarouayache","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4563","DOI":"10.1007\/s12206-022-0819-x","article-title":"Rolling Bearing Composite Fault Diagnosis Method Based on EEMD Fusion Feature","volume":"36","author":"Zhao","year":"2022","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1016\/j.chaos.2008.08.018","article-title":"Intermittent and Chaotic Vibrations in a Regenerative Cutting Process","volume":"41","author":"Litak","year":"2009","journal-title":"Chaos Solitons Fractals"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"108982","DOI":"10.1016\/j.measurement.2021.108982","article-title":"Detection of Cylinder Misfire in an Aircraft Engine Using Linear and Non-Linear Signal Analysis","volume":"174","author":"Syta","year":"2021","journal-title":"Measurement"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"113763","DOI":"10.1016\/j.measurement.2023.113763","article-title":"Detection and Identification of Cylinder Misfire in Small Aircraft Engine in Different Operating Conditions by Linear and Non-Linear Properties of Frequency Components","volume":"223","author":"Syta","year":"2023","journal-title":"Measurement"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1007\/s11012-012-9631-5","article-title":"Intermittency and Multiscale Dynamics in Milling of Fiber Reinforced Composites","volume":"48","author":"Sen","year":"2013","journal-title":"Meccanica"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Lond. A"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S1793536909000047","article-title":"Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method","volume":"1","author":"Wu","year":"2009","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"205244","DOI":"10.1109\/ACCESS.2020.3037080","article-title":"Multifractal Detrended Fluctuation Analysis of Congestive Heart Failure Disease Based on Constructed Heartbeat Sequence","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1016\/j.clinph.2021.03.041","article-title":"Detrended Fluctuation Analysis in the Presurgical Evaluation of Parietal Lobe Epilepsy Patients","volume":"132","author":"Auno","year":"2021","journal-title":"Clin. Neurophysiol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.procs.2024.03.046","article-title":"Classification of Congestive Heart Failure Using Artificial Neural Network Based on Higher-Order Moments Detrended Fluctuation Analysis of Heart Rate Variability","volume":"234","author":"Mahananto","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e14410817237","DOI":"10.33448\/rsd-v10i8.17237","article-title":"Influence of Anxiety on the Heart Rate Variability of Patients in Preoperative Orthopedic Surgery","volume":"10","author":"Correa","year":"2021","journal-title":"RSD"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.gaitpost.2010.06.004","article-title":"Re-Interpreting Detrended Fluctuation Analyses of Stride-to-Stride Variability in Human Walking","volume":"32","author":"Dingwell","year":"2010","journal-title":"Gait Posture"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1088\/0967-3334\/35\/3\/429","article-title":"Removal of Artifacts in Knee Joint Vibroarthrographic Signals Using Ensemble Empirical Mode Decomposition and Detrended Fluctuation Analysis","volume":"35","author":"Wu","year":"2014","journal-title":"Physiol. Meas."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"011114","DOI":"10.1103\/PhysRevE.64.011114","article-title":"Effect of Trends on Detrended Fluctuation Analysis","volume":"64","author":"Hu","year":"2001","journal-title":"Phys. Rev. E"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Kirchner, M., Schubert, P., Liebherr, M., and Haas, C.T. (2014). Detrended Fluctuation Analysis and Adaptive Fractal Analysis of Stride Time Data in Parkinson\u2019s Disease: Stitching Together Short Gait Trials. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0085787"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.gaitpost.2022.02.019","article-title":"Comparing Adaptive Fractal and Detrended Fluctuation Analyses of Stride Time Variability: Tests of Equivalence","volume":"94","author":"Hollman","year":"2022","journal-title":"Gait Posture"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ravi, D.K., Marmelat, V., Taylor, W.R., Newell, K.M., Stergiou, N., and Singh, N.B. (2020). Assessing the Temporal Organization of Walking Variability: A Systematic Review and Consensus Guidelines on Detrended Fluctuation Analysis. Front. Physiol., 11.","DOI":"10.3389\/fphys.2020.00562"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2793","DOI":"10.1007\/s11831-023-09898-w","article-title":"Convolutional Neural Network in Medical Image Analysis: A Review","volume":"30","author":"Kshatri","year":"2023","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Shen, M., Wen, P., Song, B., and Li, Y. (2023). Real-Time Epilepsy Seizure Detection Based on EEG Using Tunable-Q Wavelet Transform and Convolutional Neural Network. Biomed. Signal Process. Control., 82.","DOI":"10.1016\/j.bspc.2022.104566"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chen, J., Yu, K., Wang, F., Zhou, Z., Bi, Y., Zhuang, S., and Zhang, D. (2024). Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System. Electronics, 13.","DOI":"10.3390\/electronics13071310"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2316673","DOI":"10.1002\/adfm.202316673","article-title":"Point Calculation Detection System Based on In Suit Convolution Transistor for Clinical Diagnosis of Heart Disease","volume":"34","author":"Cheng","year":"2024","journal-title":"Adv. Funct. Mater."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"102459","DOI":"10.1016\/j.artmed.2022.102459","article-title":"Real-Time Detection of Freezing of Gait in Parkinson\u2019s Disease Using Multi-Head Convolutional Neural Networks and a Single Inertial Sensor","volume":"135","author":"Sigcha","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Lou, A., Guan, S., and Loew, M. (2023). CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation. Comput. Biol. Med., 154.","DOI":"10.1016\/j.compbiomed.2023.106579"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wang, T., Lu, C., Sun, Y., Yang, M., Liu, C., and Ou, C. (2021). Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network. Entropy, 23.","DOI":"10.3390\/e23010119"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1109\/TASE.2022.3185706","article-title":"Prediction of Contralateral Lower-Limb Joint Angles Using Vibroarthrography and Surface Electromyography Signals in Time-Series Network","volume":"20","author":"Wang","year":"2023","journal-title":"IEEE Trans. Automat. Sci. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1007\/s11517-018-1785-4","article-title":"Vibroarthrography for Early Detection of Knee Osteoarthritis Using Normalized Frequency Features","volume":"56","author":"Befrui","year":"2018","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_64","unstructured":"Bhat, B., and Nageshwaran, S. (2020, January 27\u201328). A Diagnostic Approach for Osteoarthritis Using Vibroarthrography. Proceedings of the 10th International Conference on Application of Information and Communication Technology and Statistics in Economy and Education ICAICTSEE\u20132020, Sofia, Bulgaria."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"7225","DOI":"10.1109\/TCE.2024.3410253","article-title":"VAG Signal Based Computational System for Consumer\u2019s Utilization Devices in Osteoarthritis Data Extraction and Classification","volume":"70","author":"Balajee","year":"2024","journal-title":"IEEE Trans. Consumer Electron."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.clinbiomech.2020.01.021","article-title":"The Detection of Knee Joint Sounds at Defined Loads by Means of Vibroarthrography","volume":"74","author":"Kalo","year":"2020","journal-title":"Clin. Biomech."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s11517-007-0278-7","article-title":"Screening of Knee-Joint Vibroarthrographic Signals Using Statistical Parameters and Radial Basis Functions","volume":"46","author":"Rangayyan","year":"2008","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1109\/10.844228","article-title":"Adaptive Time-Frequency Analysis of Knee Joint Vibroarthrographic Signals for Noninvasive Screening of Articular Cartilage Pathology","volume":"47","author":"Krishnan","year":"2000","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1109\/10.641334","article-title":"Parametric Representation and Screening of Knee Joint Vibroarthrographic Signals","volume":"44","author":"Rangayyan","year":"1997","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1007\/s10439-008-9601-1","article-title":"Analysis of Vibroarthrographic Signals with Features Related to Signal Variability and Radial-Basis Functions","volume":"37","author":"Rangayyan","year":"2009","journal-title":"Ann. Biomed. Eng."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.bspc.2009.03.008","article-title":"Screening of Knee-Joint Vibroarthrographic Signals Using Probability Density Functions Estimated with Parzen Windows","volume":"5","author":"Rangayyan","year":"2010","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/BF02510977","article-title":"Adaptive Filtering, Modelling and Classification of Knee Joint Vibroarthrographic Signals for Non-Invasive Diagnosis of Articular Cartilage Pathology","volume":"35","author":"Krishnan","year":"1997","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/0952813X.2010.506288","article-title":"Combining Least-Squares Support Vector Machines for Classification of Biomedical Signals: A Case Study with Knee-Joint Vibroarthrographic Signals","volume":"23","author":"Wu","year":"2011","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.cmpb.2008.12.012","article-title":"An Enhanced Algorithm for Knee Joint Sound Classification Using Feature Extraction Based on Time-Frequency Analysis","volume":"94","author":"Kim","year":"2009","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.compeleceng.2018.08.019","article-title":"Analysis of Knee-Joint Vibroarthographic Signals Using Bandwidth-Duration Localized Three-Channel Filter Bank","volume":"72","author":"Sharma","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1016\/j.medengphy.2014.07.008","article-title":"Representation of Fluctuation Features in Pathological Knee Joint Vibroarthrographic Signals Using Kernel Density Modeling Method","volume":"36","author":"Yang","year":"2014","journal-title":"Med. Eng. Phys."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2016.03.021","article-title":"Quantification of Knee Vibroarthrographic Signal Irregularity Associated with Patellofemoral Joint Cartilage Pathology Based on Entropy and Envelope Amplitude Measures","volume":"130","author":"Wu","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.cmpb.2017.10.027","article-title":"Analysis and Multiclass Classification of Pathological Knee Joints Using Vibroarthrographic Signals","volume":"154","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Athavale, Y., and Krishnan, S. (2020). A Telehealth System Framework for Assessing Knee-Joint Conditions Using Vibroarthrographic Signals. Biomed. Signal Process. Control., 55.","DOI":"10.1016\/j.bspc.2019.101580"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Shidore, M.M., Athreya, S.S., Deshpande, S., and Jalnekar, R. (2021). Screening of Knee-Joint Vibroarthrographic Signals Using Time and Spectral Domain Features. Biomed. Signal Process. Control., 68.","DOI":"10.1016\/j.bspc.2021.102808"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zheng, T., Song, J., and Gao, W. (2021). A Novel Automatic Knee Osteoarthritis Detection Method Based on Vibroarthrographic Signals. Biomed. Signal Process. Control., 68.","DOI":"10.1016\/j.bspc.2021.102796"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Mascarenhas, E., Nalband, S., Fredo, A.R.J., and Prince, A.A. (2020, January 27\u201328). Analysis and Classification of Vibroarthrographic Signals Using Tuneable \u2018Q\u2019 Wavelet Transform. Proceedings of the 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India.","DOI":"10.1109\/SPIN48934.2020.9071335"},{"key":"ref_83","first-page":"33","article-title":"Classification of Knee Arthropathy with Accelerometer-Based Vibroarthrography","volume":"224","author":"Moreira","year":"2016","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"19","DOI":"10.12913\/22998624\/189512","article-title":"Application of Recurrence Quantification Analysis in the Detection of Osteoarthritis of the Knee with the Use of Vibroarthrography","volume":"18","author":"Machrowska","year":"2024","journal-title":"Adv. Sci. Technol. Res. J."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"71","DOI":"10.35784\/acs-2022-14","article-title":"Knee joint osteoarthritis diagnosis based on selected acoustic signal discriminants using machine learning","volume":"18","year":"2022","journal-title":"ACS"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1109\/TBME.2021.3124487","article-title":"Quantifying Asymmetry Between Medial and Lateral Compartment Knee Loading Forces Using Acoustic Emissions","volume":"69","author":"Jeong","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Pan, T., Ye, Y., Wan, Z., Liu, B., and Ding, T. (2024). Multiscale-Temporal Feature Extraction and Boundary Confusion Alleviation for VAG-Based Fine-Grained Multi-Grade Osteoarthritis Deterioration Monitoring. Comput. Methods Programs Biomed., 255.","DOI":"10.1016\/j.cmpb.2024.108286"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Nsugbe, E., Olorunlambe, K., and Dearn, K. (2023). On the Early and Affordable Diagnosis of Joint Pathologies Using Acoustic Emissions, Deep Learning Decompositions and Prediction Machines. Sensors, 23.","DOI":"10.3390\/s23094449"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/TBME.2021.3127030","article-title":"A Feasibility Study on Tribological Origins of Knee Acoustic Emissions","volume":"69","author":"Gharehbaghi","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"9128","DOI":"10.1109\/JSEN.2018.2869990","article-title":"Using Knee Acoustical Emissions for Sensing Joint Health in Patients with Juvenile Idiopathic Arthritis: A Pilot Study","volume":"18","author":"Semiz","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Olorunlambe, K.A., Hua, Z., Shepherd, D.E.T., and Dearn, K.D. (2021). Towards a Diagnostic Tool for Diagnosing Joint Pathologies: Supervised Learning of Acoustic Emission Signals. Sensors, 21.","DOI":"10.3390\/s21238091"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1109\/TBME.2017.2743562","article-title":"Acoustical Emission Analysis by Unsupervised Graph Mining: A Novel Biomarker of Knee Health Status","volume":"65","author":"Hersek","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"17219","DOI":"10.1109\/JSEN.2024.3382613","article-title":"Validating Joint Acoustic Emissions Models as a Generalizable Predictor of Joint Health","volume":"24","author":"Richardson","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_94","unstructured":"Kraft, D., and Bieber, G. (July, January 30). Vibroarthrography Using Convolutional Neural Networks. Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Corfu, Greece."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/3\/706\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:35:33Z","timestamp":1759919733000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/3\/706"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,24]]},"references-count":94,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["s25030706"],"URL":"https:\/\/doi.org\/10.3390\/s25030706","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,24]]}}}