{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T04:15:26Z","timestamp":1774412126416,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry for University and Research","award":["ARS01_01120"],"award-info":[{"award-number":["ARS01_01120"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sarcopenia is a geriatric condition characterized by a loss of strength and muscle mass, with a high impact on health status, functional independence and quality of life in older adults. [d=TT, ]To reduce the effects of the disease, just the diagnostic is not enough, it is necessary more than recognition.To reduce the effects of the disease, it is important to recognize the level and progression of sarcopenia early. Surface electromyography is becoming increasingly relevant for the prevention and diagnosis of sarcopenia, also due to a wide diffusion of smart and minimally invasive wearable devices suitable for electromyographic monitoring. The purpose of this work is manifold. The first aim is the design and implementation of a hardware\/software platform. It is based on the elaboration of surface electromyographic signals extracted from the Gastrocnemius Lateralis and Tibialis Anterior muscles, useful to analyze the strength of the muscles with the purpose of distinguishing three different \u201cconfidence\u201d levels of sarcopenia. The second aim is to compare the efficiency of state of the art supervised classifiers in the evaluation of sarcopenia. The experimentation stage was performed on an \u201caugmented\u201d dataset starting from data acquired from 32 patients. The latter were distributed in an unbalanced manner on 3 \u201cconfidence\u201d levels of sarcopenia. The obtained results in terms of classification accuracy demonstrated the ability of the proposed platform to distinguish different sarcopenia \u201cconfidence\u201d levels, with highest accuracy value given by Support Vector Machine classifier, outperforming the other classifiers by an average of 7.7%.<\/jats:p>","DOI":"10.3390\/s22072721","type":"journal-article","created":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T21:23:55Z","timestamp":1648848235000},"page":"2721","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Comparative Analysis of Supervised Classifiers for the Evaluation of Sarcopenia Using a sEMG-Based Platform"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8970-3313","authenticated-orcid":false,"given":"Alessandro","family":"Leone","sequence":"first","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3374-2433","authenticated-orcid":false,"given":"Gabriele","family":"Rescio","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5716-5824","authenticated-orcid":false,"given":"Andrea","family":"Manni","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1312-4593","authenticated-orcid":false,"given":"Pietro","family":"Siciliano","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0318-8347","authenticated-orcid":false,"given":"Andrea","family":"Caroppo","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,1]]},"reference":[{"key":"ref_1","unstructured":"United Nations, Department of Economic and Social Affairs, Population Division (2020) (2021, December 13). World Population Ageing 2019 (ST\/ESA\/SER. A\/444). Available online: https:\/\/www.un.org\/en\/development\/desa\/population\/publications\/pdf\/ageing\/WorldPopulationAgeing2019-Report.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/0022-510X(88)90132-3","article-title":"What is the cause of the ageing atrophy?: Total number, size and proportion of different fiber types studied in whole vastus lateralis muscle from 15-to 83-year-old men","volume":"84","author":"Lexell","year":"1988","journal-title":"J. Neurol. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"990S","DOI":"10.1093\/jn\/127.5.990S","article-title":"Sarcopenia: Origins and clinical relevance","volume":"127","author":"Rosenberg","year":"1997","journal-title":"J. Nutr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1097\/MCO.0000000000000011","article-title":"Dietary protein and muscle in older persons","volume":"17","author":"Leidy","year":"2014","journal-title":"Curr. Opin. Clin. Nutr. Metab. Care"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s13539-010-0014-2","article-title":"An overview of sarcopenia: Facts and numbers on prevalence and clinical impact","volume":"1","author":"Morley","year":"2010","journal-title":"J. Cachexia Sarcopenia Muscle"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/2049-3258-72-45","article-title":"Sarcopenia: Burden and challenges for public health","volume":"72","author":"Beaudart","year":"2014","journal-title":"Arch. Public Health"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1111\/j.1532-5415.2004.52014.x","article-title":"The healthcare costs of sarcopenia in the United States","volume":"52","author":"Janssen","year":"2004","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1016\/j.jamcollsurg.2013.04.042","article-title":"Cost of major surgery in the sarcopenic patient","volume":"217","author":"Sheetz","year":"2013","journal-title":"J. Am. Coll. Surg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1177\/0884533615569888","article-title":"Implications of sarcopenia in major surgery","volume":"30","author":"Friedman","year":"2015","journal-title":"Nutr. Clin. Pract."},{"key":"ref_10","first-page":"217","article-title":"Optimal management of sarcopenia","volume":"5","author":"Burton","year":"2010","journal-title":"Clin. Interv. Aging"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1007\/s40520-016-0589-3","article-title":"The role of DXA in sarcopenia","volume":"28","author":"Guglielmi","year":"2016","journal-title":"Aging Clin. Exp. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1093\/ageing\/afy169","article-title":"Sarcopenia: Revised European consensus on definition and diagnosis","volume":"48","author":"Bahat","year":"2019","journal-title":"Age Ageing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2766","DOI":"10.1111\/ajt.13832","article-title":"Comparing the Variability Between Measurements for Sarcopenia Using Magnetic Resonance Imaging and Computed Tomography Imaging","volume":"16","author":"Tandon","year":"2016","journal-title":"Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/TITB.2009.2035050","article-title":"A body sensor network with electromyogram and inertial sensors: Multimodal interpretation of muscular activities","volume":"14","author":"Ghasemzadeh","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"12431","DOI":"10.3390\/s130912431","article-title":"Surface electromyography signal processing and classification techniques","volume":"13","author":"Chowdhury","year":"2013","journal-title":"Sensors"},{"key":"ref_16","unstructured":"Giampetruzzi, L., Rescio, G., Leone, A., and Siciliano, P. (2019). Analysis of skeletal muscles contractility using smart SEMG-based socks. Italian Forum of Ambient Assisted Living, Springer."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"934","DOI":"10.3389\/fneur.2020.00934","article-title":"Surface EMG in clinical assessment and neurorehabilitation: Barriers limiting its use","volume":"11","author":"Campanini","year":"2020","journal-title":"Front. Neurol."},{"key":"ref_18","unstructured":"Yu, F., Bilberg, A., and Stenager, E. (September, January 31). Wireless medical sensor measurements of fatigue in patients with multiple sclerosis. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Varshney, S., Thakur, R., Jigyasu, R., and Narayan, Y. (2019, January 15\u201317). sEMG signal based hand and finger movement clasification using different classifiers and techniques: A Review. Proceedings of the International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India.","DOI":"10.1109\/ICCS45141.2019.9065848"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.imu.2018.06.004","article-title":"Surface electromyography based method for computing muscle strength and fatigue of biceps brachii muscle and its clinical implementation","volume":"12","author":"Kuthe","year":"2018","journal-title":"Inform. Med. Unlocked"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Askarinejad, S.E., Nazari, M.A., and Borachalou, S.R. (2018, January 28\u201330). Experimental detection of muscle atrophy initiation Using sEMG signals. Proceedings of the 2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME), Tunis, Tunisia.","DOI":"10.1109\/MECBME.2018.8402402"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bagherian Azhiri, R., Esmaeili, M., and Nourani, M. (2021). Real-Time EMG Signal Classification via Recurrent Neural Networks. arXiv.","DOI":"10.1109\/BIBM52615.2021.9669872"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3389\/fnbot.2019.00031","article-title":"SVM-based classification of sEMG signals for upper-limb self-rehabilitation training","volume":"13","author":"Cai","year":"2019","journal-title":"Front. Neurorobot."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"125","DOI":"10.11005\/jbm.2020.27.2.125","article-title":"Evaluating Postoperative Muscle Strength Using Surface Electromyography in Hip Fracture Patient","volume":"27","author":"Yoo","year":"2020","journal-title":"J. Bone Metab."},{"key":"ref_26","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_27","unstructured":"(2021, October 10). BTS Bioengeneering. Available online: https:\/\/www.btsbioengineering.com\/products\/freeemg\/."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.jelekin.2014.07.015","article-title":"Muscle co-contraction around the knee when walking with unstable shoes","volume":"25","author":"Horsak","year":"2015","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"278","DOI":"10.18857\/jkpt.2021.33.6.278","article-title":"Effects of Open Kinetic Chain Exercise for the Gastrocnemius and Tibialis Anterior Muscles on Balance","volume":"33","author":"Yi","year":"2021","journal-title":"J. Korean Phys. Ther."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1016\/j.jamda.2014.04.021","article-title":"Validating the SARC-F: A suitable community screening tool for sarcopenia?","volume":"15","author":"Woo","year":"2014","journal-title":"J. Am. Med. Dir. Assoc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1007\/s12603-020-1462-9","article-title":"SARC-F as a screening tool for sarcopenia and possible sarcopenia proposed by AWGS 2019 in hospitalized older adults","volume":"24","author":"Ishida","year":"2020","journal-title":"J. Nutr. Health Aging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1007\/s40520-020-01782-y","article-title":"SARC-F as a case-finding tool for sarcopenia according to the EWGSOP2. National validation and comparison with other diagnostic standards","volume":"33","author":"Piotrowicz","year":"2021","journal-title":"Aging Clin. Exp. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A study of the behavior of several methods for balancing machine learning training data","volume":"6","author":"Batista","year":"2004","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"944","DOI":"10.19026\/rjaset.5.5044","article-title":"Imbalanced classification based on active learning SMOTE","volume":"5","author":"Mi","year":"2013","journal-title":"Res. J. Appl. Sci. Eng. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","article-title":"Asymptotic properties of nearest neighbor rules using edited data","volume":"2","author":"Wilson","year":"1972","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1016\/j.jbiomech.2010.01.027","article-title":"Filtering the surface EMG signal: Movement artifact and baseline noise contamination","volume":"43","author":"Gilmore","year":"2010","journal-title":"J. Biomech."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Barioul, R., Fakhfakh, S., Derbel, H., and Kanoun, O. (2019, January 21\u201324). Evaluation of EMG signal time domain features for hand gesture distinction. Proceedings of the 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD), Istanbul, Turkey.","DOI":"10.1109\/SSD.2019.8893277"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.clinbiomech.2009.01.010","article-title":"Surface EMG based muscle fatigue evaluation in biomechanics","volume":"24","author":"Cifrek","year":"2009","journal-title":"Clin. Biomech."},{"key":"ref_39","first-page":"71","article-title":"A novel feature extraction for robust EMG pattern recognition","volume":"1","author":"Phinyomark","year":"2009","journal-title":"J. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Phinyomark, A., Chujit, G., Phukpattaranont, P., Limsakul, C., and Hu, H. (2012, January 16\u201318). A preliminary study assessing time-domain EMG features of classifying exercises in preventing falls in the elderly. Proceedings of the 2012 9th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Phetchaburi, Thailand.","DOI":"10.1109\/ECTICon.2012.6254117"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"140053","DOI":"10.1038\/sdata.2014.53","article-title":"Electromyography data for non-invasive naturally-controlled robotic hand prostheses","volume":"1","author":"Atzori","year":"2014","journal-title":"Sci. Data"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Too, J., Abdullah, A.R., Mohd Saad, N., and Mohd Ali, N. (2018). Feature selection based on binary tree growth algorithm for the classification of myoelectric signals. Machines, 6.","DOI":"10.3390\/machines6040065"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least squares support vector machine classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 algorithms in data mining","volume":"14","author":"Wu","year":"2008","journal-title":"Knowl. Inf. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2000","journal-title":"Mach. Learn."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hosmer, D.W., Lemeshow, S., and Sturdivant, R.X. (2013). Applied Logistic Regression, John Wiley & Sons.","DOI":"10.1002\/9781118548387"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1109\/TNNLS.2017.2673241","article-title":"Efficient kNN classification with different numbers of nearest neighbors","volume":"5","author":"Zhang","year":"2018","journal-title":"IEEE Transac Neural Networks Learning Systems"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.knosys.2006.11.008","article-title":"A decision tree-based attribute weighting filter for naive Bayes","volume":"20","author":"Hall","year":"2007","journal-title":"Knowl. -Based Syst."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Albarakati, N., and Kecman, V. (2013, January 4\u20137). Fast neural network algorithm for solving classification tasks: Batch error back-propagation algorithm. Proceedings of the IEEE International Conference on Southeastcon, Jacksonville, FL, USA.","DOI":"10.1109\/SECON.2013.6567409"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.cpc.2018.02.018","article-title":"Optimizing event selection with the random grid search","volume":"228","author":"Bhat","year":"2018","journal-title":"Comput. Phys. Commun."},{"key":"ref_52","unstructured":"Grandini, M., Bagli, E., and Visani, G. (2020). Metrics for multi-class classification: An overview. arXiv."},{"key":"ref_53","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Science & Business Media."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2721\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:48:27Z","timestamp":1760136507000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2721"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,1]]},"references-count":53,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22072721"],"URL":"https:\/\/doi.org\/10.3390\/s22072721","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,1]]}}}