{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T12:11:47Z","timestamp":1775477507932,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Council for Scientific and Technological Development (CNPq)","award":["88887.892501\/2023-00"],"award-info":[{"award-number":["88887.892501\/2023-00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Muscle tone is defined as the resistance to passive stretch, but this definition is often criticized for its ambiguity since some suggest it is related to a state of preparation for movement. Muscle tone is primarily regulated by the central nervous system, and individuals with neurological disorders may lose the ability to control normal tone and can exhibit abnormalities. Currently, these abnormalities are mostly evaluated using subjective scales, highlighting a lack of objective assessment methods in the literature. This study aimed to use surface electromyography (sEMG) and machine learning (ML) for the objective classification and characterization of the full spectrum of muscle tone in the upper limb. Data were collected from thirty-nine individuals, including spastic, healthy, hypotonic and rigid subjects. All of the classifiers applied achieved high accuracy, with the best reaching 96.12%, in differentiating muscle tone. These results underscore the potential of the proposed methodology as a more reliable and quantitative method for evaluating muscle tone abnormalities, aiming to address the limitations of traditional subjective assessments. Additionally, the main features impacting the classifiers\u2019 performance were identified, which can be utilized in future research and in the development of devices that can be used in clinical practice.<\/jats:p>","DOI":"10.3390\/s24196362","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T12:06:32Z","timestamp":1727697992000},"page":"6362","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Muscle Tone Assessment by Machine Learning Using Surface Electromyography"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5571-2764","authenticated-orcid":false,"given":"Andressa Rastrelo","family":"Rezende","sequence":"first","affiliation":[{"name":"Assistive Technology Laboratory, Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38400-902, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7509-1547","authenticated-orcid":false,"given":"Camille Marques","family":"Alves","sequence":"additional","affiliation":[{"name":"Assistive Technology Laboratory, Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38400-902, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5025-7328","authenticated-orcid":false,"given":"Isabela Alves","family":"Marques","sequence":"additional","affiliation":[{"name":"Assistive Technology Laboratory, Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38400-902, Brazil"}]},{"given":"Luciane Aparecida Pascucci Sande","family":"de Souza","sequence":"additional","affiliation":[{"name":"Department of Applied Physical Therapy, Federal University of Triangulo Mineiro, Uberaba 38065-430, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4175-723X","authenticated-orcid":false,"given":"Eduardo L\u00e1zaro Martins","family":"Naves","sequence":"additional","affiliation":[{"name":"Assistive Technology Laboratory, Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38400-902, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e89","DOI":"10.1542\/peds.111.1.e89","article-title":"Task Force on Childhood Motor Disorders. Classification and definition of disorders causing hypertonia in childhood","volume":"111","author":"Sanger","year":"2003","journal-title":"Pediatrics"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shortland, A.P. (2018). Muscle tone is not a well-defined term. Dev. Med. Child Neurol., 60.","DOI":"10.1111\/dmcn.13707"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.humov.2017.11.013","article-title":"Bernstein\u2019s levels of movement construction: A contemporary perspective","volume":"57","author":"Profeta","year":"2018","journal-title":"Hum. Mov. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.ijotn.2016.01.002","article-title":"Neurological assessment","volume":"22","author":"Maher","year":"2016","journal-title":"Int. J. Orthop. Trauma Nurs."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e66","DOI":"10.1542\/pir.30.9.e66","article-title":"The floppy infant: Evaluation of hypotonia","volume":"30","author":"Peredo","year":"2009","journal-title":"Pediatr. Rev."},{"key":"ref_6","unstructured":"Lance, J.W. (1980). Symposium synopsis. Spasticity: Disordered Motor Control, Year Book Medical."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Martino, D., Espay, A.J., Fasano, A., and Morgante, F. (2016). Abnormalities of muscle tone. Disorders of Movement: A Guide to Diagnosis and Treatment, Springer.","DOI":"10.1007\/978-3-662-48468-5"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"S13","DOI":"10.1212\/WNL.0b013e3182762448","article-title":"Toward an epidemiology of poststroke spasticity","volume":"80","author":"Wissel","year":"2013","journal-title":"Neurology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1093\/ptj\/67.2.206","article-title":"Interrater reliability of a modified Ashworth scale of muscle spasticity","volume":"67","author":"Bohannon","year":"1987","journal-title":"Phys. Ther."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1177\/1545968311423668","article-title":"Botulinum toxin effect on voluntary and stretch reflex-related torque produced by the quadriceps: An isokinetic pilot study","volume":"26","author":"Bernuz","year":"2012","journal-title":"Neurorehabil. Neural Repair"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1097\/00002060-199807000-00003","article-title":"Effects of surface spinal cord stimulation on spasticity and quantitative","volume":"77","author":"Wang","year":"1998","journal-title":"Am. J. Phys. Med. Rehabil."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1080\/09638288.2017.1381183","article-title":"Measurement of post-stroke spasticity based on tonic stretch reflex threshold: Implications of stretch velocity for clinical practice","volume":"41","author":"Marques","year":"2017","journal-title":"Disabil. Rehabil."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3354","DOI":"10.1016\/j.ridd.2014.07.053","article-title":"The relation between spasticity and muscle behavior during the swing phase of gait in children with cerebral palsy","volume":"35","author":"Molenaers","year":"2014","journal-title":"Res. Dev. Disabil."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"536","DOI":"10.23736\/S1973-9087.17.04815-8","article-title":"Technologically-advanced assessment of upper-limb spasticity: A pilot study","volume":"54","author":"Posteraro","year":"2018","journal-title":"Eur. J. Phys. Rehabil. Med."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pilla, A., Trigili, E., McKinney, Z., Fanciullacci, C., Malasoma, C., Posteraro, F., Crea, S., and Vitiello, N. (2020). Robotic Rehabilitation and Multimodal Instrumented Assessment of Post-stroke Elbow Motor Functions\u2014A Randomized Controlled Trial Protocol. Front. Neurol., 11.","DOI":"10.3389\/fneur.2020.587293"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1016\/j.apmr.2010.01.012","article-title":"Higher muscle passive stiffness in Parkinson\u2019s disease patients than in controls measured by myotonometry","volume":"91","author":"Marusiak","year":"2010","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_17","first-page":"1","article-title":"Quantitative assessment of muscle stiffness using Tensiomyography before and after injection of botulinum toxin type A in patients after stroke","volume":"4","author":"Mikami","year":"2019","journal-title":"Phys. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.clinbiomech.2017.03.012","article-title":"The effect of subthalamic stimulation on viscoelastic stiffness of skeletal muscles in patients with Parkinson\u2019s disease","volume":"44","author":"Asser","year":"2017","journal-title":"Clin. Biomech."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1016\/j.clinimag.2016.05.008","article-title":"Ultrasound shear wave elastography in assessment of muscle stiffness in patients with Parkinson\u2019s disease: A primary observation","volume":"40","author":"Du","year":"2016","journal-title":"Clin. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, Y., Yu, S., Cai, Q., Huang, S., Ma, K., Zheng, H., and Xie, L. (2021). A spasticity assessment method for voluntary movement using data fusion and machine learning. Biomed. Signal Process. Control, 65.","DOI":"10.1016\/j.bspc.2020.102353"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/0165-1781(89)90019-X","article-title":"Asymmetry of neuroleptic-induced rigidity: Development of quantitative methods and clinical correlates","volume":"30","author":"Caligiuri","year":"1989","journal-title":"Psychiatry Res."},{"key":"ref_22","first-page":"256","article-title":"Measurement of muscle tone in children with cerebellar ataxia","volume":"71","author":"Iloeje","year":"1994","journal-title":"East Afr. Med. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1177\/088307389601100116","article-title":"Neurologic findings in children and adults with Williams syndrome","volume":"11","author":"Chapman","year":"1996","journal-title":"J. Child Neurol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_25","first-page":"8","article-title":"Standards for surface electromyography: The European project Surface EMG for non-invasive assessment of muscles (SENIAM)","volume":"10","author":"Stegeman","year":"2007","journal-title":"Enschede Roessingh Res. Dev."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alves, C.M., Rezende, A.R., Marques, I.A., and Naves, E.L.M. (2021). SpES: A new portable device for objective assessment of hypertonia in clinical practice. Comput. Biol. Med., 134.","DOI":"10.1016\/j.compbiomed.2021.104486"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rezende, A., Alves, C., Marques, I.A., Silva, M.A., and Naves, E. (2018). Polymer Optical Fiber Goniometer: A New Portable, Low Cost and Reliable Sensor for Joint Analysis. Sensors, 18.","DOI":"10.3390\/s18124293"},{"key":"ref_28","unstructured":"RStudio Team (2020). RStudio: Integrated Development Environment for R, RStudio, PBC. Available online: http:\/\/www.rstudio.com\/."},{"key":"ref_29","first-page":"41","article-title":"Study of K-nearest neighbour classification performance on fatigue and non-fatigue EMG signal features","volume":"11","author":"Bukhari","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Aviles, M., S\u00e1nchez-Reyes, L.-M., Fuentes-Aguilar, R.Q., Toledo-P\u00e9rez, D.C., and Rodr\u00edguez-Res\u00e9ndiz, J. (2022). A novel methodology for classifying EMG movements based on SVM and genetic algorithms. Micromachines, 13.","DOI":"10.3390\/mi13122108"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Turgunov, A., Zohirov, K., Ganiyev, A., and Sharopova, B. (2020, January 29\u201331). Defining the features of EMG signals on the forearm of the hand using SVM, RF, k-NN classification algorithms. Proceedings of the 2020 Information Communication Technologies Conference (ICTC), Nanjing, China.","DOI":"10.1109\/ICTC49638.2020.9123287"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Javaid, H.A., Tiwana, M.I., Alsanad, A., Iqbal, J., Riaz, M.T., Ahmad, S., and Almisned, F.A. (2021). Classification of hand movements using MYO armband on an embedded platform. Electronics, 10.","DOI":"10.3390\/electronics10111322"},{"key":"ref_33","first-page":"103","article-title":"Efeitos do fortalecimento muscular e sua rela\u00e7\u00e3o com a atividade funcional e a espasticidade em indiv\u00edduos hemipar\u00e9ticos","volume":"8","author":"Junqueira","year":"2004","journal-title":"Rev. Bras. Fisioter."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Alves, C.M., Rezende, A.R., Marques, I.A., Mendes, L.C., de S\u00e1, A.A.R., Vieira, M.F., J\u00fanior, E.A.L., Pereira, A.A., Oliveira, F.H.M., and de Souza, L.P.S. (2022). Wrist rigidity evaluation in Parkinson\u2019s disease: A scoping review. Healthcare, 10.","DOI":"10.3390\/healthcare10112178"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, G., Zhao, H., Fan, F., Liu, G., Xu, Q., and Nazir, S. (2022). An enhanced intrusion detection model based on improved kNN in WSNs. Sensors, 22.","DOI":"10.3390\/s22041407"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"M\u00farias Lopes, E., Vilas-Boas, M.C., Dias, D., Rosas, M.J., Vaz, R., and Silva Cunha, J.P. (2020). IHandU: A novel quantitative wrist rigidity evaluation device for deep brain stimulation surgery. Sensors, 20.","DOI":"10.3390\/s20020331"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"8019232","DOI":"10.1155\/2018\/8019232","article-title":"On the Use of t-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification of Individuals with Parkinson\u2019s Disease","volume":"2018","author":"Oliveira","year":"2018","journal-title":"Comput. Math. Methods Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1136\/jnnp.46.1.45","article-title":"Physiological mechanisms of rigidity in Parkinson\u2019s disease","volume":"46","author":"Berardelli","year":"1983","journal-title":"J. Neurol. Neurosurg. Psychiatry"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6362\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:08:09Z","timestamp":1760112489000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6362"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"references-count":38,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24196362"],"URL":"https:\/\/doi.org\/10.3390\/s24196362","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,30]]}}}