{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:34:41Z","timestamp":1776890081862,"version":"3.51.2"},"reference-count":40,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T00:00:00Z","timestamp":1646179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Marie Sk\u0142odowska-Curie Actions (MSCA)","award":["H2020-MSCA-IF-2019-899040"],"award-info":[{"award-number":["H2020-MSCA-IF-2019-899040"]}]},{"DOI":"10.13039\/100014419","name":"EIT Health","doi-asserted-by":"publisher","award":["20682"],"award-info":[{"award-number":["20682"]}],"id":[{"id":"10.13039\/100014419","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Working towards the development of robust motion recognition systems for assistive technology control, the widespread approach has been to use a plethora of, often times, multi-modal sensors. In this paper, we develop single-sensor motion recognition systems. Utilising the peripheral nature of surface electromyography (sEMG) data acquisition, we optimise the information extracted from sEMG sensors. This allows the reduction in sEMG sensors or provision of contingencies in a system with redundancies. In particular, we process the sEMG readings captured at the trapezius descendens and platysma muscles. We demonstrate that sEMG readings captured at one muscle contain distinct information on movements or contractions of other agonists. We used the trapezius and platysma muscle sEMG data captured in able-bodied participants and participants with tetraplegia to classify shoulder movements and platysma contractions using white-box supervised learning algorithms. Using the trapezius sensor, shoulder raise is classified with an accuracy of 99%. Implementing subject-specific multi-class classification, shoulder raise, shoulder forward and shoulder backward are classified with a 94% accuracy amongst object raise and shoulder raise-and-hold data in able bodied adults. A three-way classification of the platysma sensor data captured with participants with tetraplegia achieves a 95% accuracy on platysma contraction and shoulder raise detection.<\/jats:p>","DOI":"10.3390\/s22051939","type":"journal-article","created":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T22:53:25Z","timestamp":1646261605000},"page":"1939","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Frequency-Domain sEMG Classification Using a Single Sensor"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9979-7800","authenticated-orcid":false,"given":"Thekla","family":"Stefanou","sequence":"first","affiliation":[{"name":"Camin Team, National Institute for Research in Computer Science and Automation (Inria), 34090 Montpellier, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4184-6243","authenticated-orcid":false,"given":"David","family":"Guiraud","sequence":"additional","affiliation":[{"name":"Camin Team, National Institute for Research in Computer Science and Automation (Inria), 34090 Montpellier, France"},{"name":"Neurinnov, 34600 Les Aires, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3042-0941","authenticated-orcid":false,"given":"Charles","family":"Fattal","sequence":"additional","affiliation":[{"name":"Camin Team, National Institute for Research in Computer Science and Automation (Inria), 34090 Montpellier, France"},{"name":"Rehabilitation Center Bouffard Vercelli, USSAP, 66000 Perpignan, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7379-8004","authenticated-orcid":false,"given":"Christine","family":"Azevedo-Coste","sequence":"additional","affiliation":[{"name":"Camin Team, National Institute for Research in Computer Science and Automation (Inria), 34090 Montpellier, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7255-5916","authenticated-orcid":false,"given":"Lucas","family":"Fonseca","sequence":"additional","affiliation":[{"name":"Camin Team, National Institute for Research in Computer Science and Automation (Inria), 34090 Montpellier, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,2]]},"reference":[{"key":"ref_1","unstructured":"Nakauchi, Y., Noguchi, K., Somwong, P., Matsubara, T., and Namatame, A. (2003, January 27\u201331). Vivid room: Human intention detection and activity support environment for ubiquitous autonomy. Proceedings of the 2003 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), Las Vegas, NV, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1111\/ner.12286","article-title":"Coordinating Upper and Lower Body during FES-Assisted Transfers in Persons with Spinal Cord Injury in Order to Reduce Arm Support","volume":"18","author":"Jovic","year":"2015","journal-title":"Neuromodul. Technol. Neural Interface"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fanfeng, Z. (2010, January 4\u20136). Application Research of Voice Control in Reading Assistive Device for Visually Impaired Persons. Proceedings of the 2010 International Conference on Multimedia Information Networking and Security, Nanjing, China. ISSN 2162-8998.","DOI":"10.1109\/MINES.2010.10"},{"key":"ref_4","first-page":"2055668318773991","article-title":"Head-mounted eye gaze tracking devices: An overview of modern devices and recent advances","volume":"5","author":"Cognolato","year":"2018","journal-title":"J. Rehabil. Assist. Technol. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Novak, D., and Riener, R. (2013, January 24\u201326). Enhancing patient freedom in rehabilitation robotics using gaze-based intention detection. Proceedings of the 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR), Seattle, WA, USA. ISSN 1945-7901.","DOI":"10.1109\/ICORR.2013.6650507"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kelley, R., Tavakkoli, A., King, C., Nicolescu, M., Nicolescu, M., and Bebis, G. (2008, January 12\u201315). Understanding human intentions via hidden markov models in autonomous mobile robots. Proceedings of the 3rd ACM\/IEEE International Conference on Human Robot Interaction, HRI\u201908, Amsterdam, The Netherlands.","DOI":"10.1145\/1349822.1349870"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Fonseca, L., Tigra, W., Navarro, B., Guiraud, D., Fattal, C., B\u00f3, A., Fachin-Martins, E., Leynaert, V., G\u00e9lis, A., and Azevedo-Coste, C. (2019). Assisted Grasping in Individuals with Tetraplegia: Improving Control through Residual Muscle Contraction and Movement. Sensors, 19.","DOI":"10.3390\/s19204532"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1109\/TMECH.2011.2144614","article-title":"Mechatronic Design and Characterization of the Index Finger Module of a Hand Exoskeleton for Post-Stroke Rehabilitation","volume":"17","author":"Chiri","year":"2012","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1080\/03772063.2017.1381047","article-title":"A Review on Upper-Limb Myoelectric Prosthetic Control","volume":"64","author":"Iqbal","year":"2018","journal-title":"IETE J. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1109\/TNSRE.2016.2609478","article-title":"A Novel EMG Interface for Individuals with Tetraplegia to Pilot Robot Hand Grasping","volume":"26","author":"Tigra","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"18","DOI":"10.3389\/fnbot.2018.00018","article-title":"A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait","volume":"12","author":"Torricelli","year":"2018","journal-title":"Front. Neurorobot."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wolf, M.T., Assad, C., Stoica, A., You, K., Jethani, H., Vernacchia, M.T., Fromm, J., and Iwashita, Y. (2013, January 2\u20139). Decoding static and dynamic arm and hand gestures from the JPL BioSleeve. Proceedings of the IEEE Aerospace Conference Proceedings, Big Sky, MT, USA. ISSN 1095323X.","DOI":"10.1109\/AERO.2013.6497171"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"60736","DOI":"10.1109\/ACCESS.2019.2913393","article-title":"Robust Human Activity Recognition Using Multimodal Feature-Level Fusion","volume":"7","author":"Javed","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3389\/fnbot.2017.00007","article-title":"Translating Research on Myoelectric Control into Clinics\u2014Are the Performance Assessment Methods Adequate?","volume":"11","author":"Vujaklija","year":"2017","journal-title":"Front. Neurorobot."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.bspc.2019.02.011","article-title":"A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration","volume":"51","author":"Bi","year":"2019","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7792","DOI":"10.1109\/ACCESS.2019.2963881","article-title":"Myoelectric Interfaces and Related Applications: Current State of EMG Signal Processing\u2014A Systematic Review","volume":"8","author":"Soto","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105721","DOI":"10.1016\/j.cmpb.2020.105721","article-title":"Shoulder muscle activation pattern recognition based on sEMG and machine learning algorithms","volume":"197","author":"Jiang","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TBME.2011.2177662","article-title":"Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration","volume":"59","author":"Young","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.bspc.2015.02.009","article-title":"Current state of digital signal processing in myoelectric interfaces and related applications","volume":"18","author":"Hakonen","year":"2015","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1109\/TBME.2008.923917","article-title":"A comparison of the effects of electrode implantation and targeting on pattern classification accuracy for prosthesis control","volume":"55","author":"Farrell","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3371","DOI":"10.1109\/TBME.2019.2904398","article-title":"A Modular, Smart, and Wearable System for High Density sEMG Detection","volume":"66","author":"Cerone","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_22","first-page":"301","article-title":"Fasciculation potentials in high-density surface EMG","volume":"24","author":"Drost","year":"2007","journal-title":"J. Clin. Neurophysiol. Off. Publ. Am. Electroencephalogr. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102548","DOI":"10.1016\/j.jelekin.2021.102548","article-title":"Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators","volume":"58","author":"Hug","year":"2021","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"026005","DOI":"10.1088\/1741-2552\/aaf4c3","article-title":"Prediction of finger kinematics from discharge timings of motor units: Implications for intuitive control of myoelectric prostheses","volume":"16","author":"Chen","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41551-016-0025","article-title":"Man\/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation","volume":"1","author":"Farina","year":"2017","journal-title":"Nat. Biomed. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"056010","DOI":"10.1088\/1741-2552\/abf186","article-title":"Simultaneous and proportional control of wrist and hand movements by decoding motor unit discharges in real time","volume":"18","author":"Chen","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"036004","DOI":"10.1088\/1741-2560\/6\/3\/036004","article-title":"Muscle synergies as a predictive framework for the EMG patterns of new hand postures","volume":"6","author":"Ajiboye","year":"2009","journal-title":"J. Neural Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1016\/j.medengphy.2011.02.006","article-title":"Synergy matrices to estimate fluid wrist movements by surface electromyography","volume":"33","author":"Choi","year":"2011","journal-title":"Med. Eng. Phys."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/TNSRE.2013.2278411","article-title":"Intuitive, Online, Simultaneous, and Proportional Myoelectric Control Over Two Degrees-of-Freedom in Upper Limb Amputees","volume":"22","author":"Jiang","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.eswa.2017.03.012","article-title":"Single channel surface EMG control of advanced prosthetic hands: A simple, low cost and efficient approach","volume":"79","author":"Tavakoli","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.bspc.2018.07.010","article-title":"Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier","volume":"46","author":"Tavakoli","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hameed, H.K., Hassan, W.Z.W., Shafie, S., Ahmad, S.A., Jaafar, H., Mat, L.N., and Alkubaisi, Y. (April, January 4). Identifying the Best Forearm Muscle to Control Soft Robotic Glove System by Using a Single sEMG Channel. Proceedings of the 2020 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates.","DOI":"10.1109\/ASET48392.2020.9118218"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bhoi, A.K., Sherpa, K.S., and Mallick, P.K. (2014, January 3\u20135). A comparative analysis of neuropathic and healthy EMG signal using PSD. Proceedings of the 2014 International Conference on Communication and Signal Processing, Melmaruvathur, India.","DOI":"10.1109\/ICCSP.2014.6950074"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"016011","DOI":"10.1088\/1741-2560\/11\/1\/016011","article-title":"Analysis of Surface EMG Baseline for Detection of Hidden Muscle Activity","volume":"11","author":"Zhang","year":"2014","journal-title":"J. Neural Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/S1050-6411(00)00027-4","article-title":"Development of Recommendations for SEMG Sensors and Sensor Placement Procedures","volume":"10","author":"Hermens","year":"2000","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"036008","DOI":"10.1088\/1741-2560\/12\/3\/036008","article-title":"Power spectrum of the rectified EMG: When and why is rectification beneficial for identifying neural connectivity?","volume":"12","author":"Negro","year":"2015","journal-title":"J. Neural Eng."},{"key":"ref_38","unstructured":"Mitchell, T. (1997). Decision Tree Learning. Machine Learning, McGraw-Hill."},{"key":"ref_39","first-page":"438","article-title":"Instance Reduction for Avoiding Overfitting in Decision Trees","volume":"30","author":"Amro","year":"2021","journal-title":"J. Intell. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Parajuli, N., Sreenivasan, N., Bifulco, P., Cesarelli, M., Savino, S., Niola, V., Esposito, D., Hamilton, T.J., Naik, G.R., and Gunawardana, U. (2019). Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation. Sensors, 19.","DOI":"10.3390\/s19204596"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1939\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:30:31Z","timestamp":1760135431000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1939"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,2]]},"references-count":40,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22051939"],"URL":"https:\/\/doi.org\/10.3390\/s22051939","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,2]]}}}