{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T16:26:25Z","timestamp":1784391985614,"version":"3.55.0"},"reference-count":45,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,24]],"date-time":"2019-06-24T00:00:00Z","timestamp":1561334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002343","name":"Institut de Recherche Robert-Sauv\u00e9 en Sant\u00e9 et en S\u00e9curit\u00e9 du Travail","doi-asserted-by":"publisher","award":["PhD grant"],"award-info":[{"award-number":["PhD grant"]}],"id":[{"id":"10.13039\/501100002343","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["01220434, 376091307, 11409"],"award-info":[{"award-number":["01220434, 376091307, 11409"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be derived by leveraging machine learning. However, the sEMG acquisition systems currently available tend to be prohibitively costly for personal use or sacrifice wearability or signal quality to be more affordable. This work introduces the 3DC Armband designed by the Biomedical Microsystems Laboratory in Laval University; a wireless, 10-channel, 1000 sps, dry-electrode, low-cost (\u223c150 USD) myoelectric armband that also includes a 9-axis inertial measurement unit. The proposed system is compared with the Myo Armband by Thalmic Labs, one of the most popular sEMG acquisition systems. The comparison is made by employing a new offline dataset featuring 22 able-bodied participants performing eleven hand\/wrist gestures while wearing the two armbands simultaneously. The 3DC Armband systematically and significantly (    p &lt; 0.05    ) outperforms the Myo Armband, with three different classifiers employing three different input modalities when using ten seconds or more of training data per gesture. This new dataset, alongside the source code, Altium project and 3-D models are made readily available for download within a Github repository.<\/jats:p>","DOI":"10.3390\/s19122811","type":"journal-article","created":{"date-parts":[[2019,6,24]],"date-time":"2019-06-24T11:01:57Z","timestamp":1561374117000},"page":"2811","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3241-8404","authenticated-orcid":false,"given":"Ulysse","family":"C\u00f4t\u00e9-Allard","sequence":"first","affiliation":[{"name":"Department of Computer and Electrical Engineering, Universit\u00e9 Laval, 1065 Avenue de la M\u00e9decine, Quebec, QC G1V 0A6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4336-2664","authenticated-orcid":false,"given":"Gabriel","family":"Gagnon-Turcotte","sequence":"additional","affiliation":[{"name":"Department of Computer and Electrical Engineering, Universit\u00e9 Laval, 1065 Avenue de la M\u00e9decine, Quebec, QC G1V 0A6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fran\u00e7ois","family":"Laviolette","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, Universit\u00e9 Laval, 1065 Avenue de la M\u00e9decine, Quebec, QC G1V 0A6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1473-3451","authenticated-orcid":false,"given":"Benoit","family":"Gosselin","sequence":"additional","affiliation":[{"name":"Department of Computer and Electrical Engineering, Universit\u00e9 Laval, 1065 Avenue de la M\u00e9decine, Quebec, QC G1V 0A6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,24]]},"reference":[{"key":"ref_1","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_2","unstructured":"Allard, U.C., Nougarou, F., Fall, C.L., Gigu\u00e8re, P., Gosselin, C., Laviolette, F., and Gosselin, B. (2016, January 9\u201314). A convolutional neural network for robotic arm guidance using semg based frequency-features. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TASLP.2017.2738568","article-title":"Emg-to-speech: Direct generation of speech from facial electromyographic signals","volume":"25","author":"Janke","year":"2017","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.bspc.2007.07.009","article-title":"Myoelectric control systems\u2014A survey","volume":"2","author":"Oskoei","year":"2007","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0208-5216(12)70039-6","article-title":"High-density surface EMG: Techniques and applications at a motor unit level","volume":"32","author":"Stegeman","year":"2012","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pizzolato, S., Tagliapietra, L., Cognolato, M., Reggiani, M., M\u00fcller, H., and Atzori, M. (2017). Comparison of six electromyography acquisition setups on hand movement classification tasks. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0186132"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Phinyomark, A., N Khushaba, R., and Scheme, E. (2018). Feature extraction and selection for myoelectric control based on wearable EMG sensors. Sensors, 18.","DOI":"10.3390\/s18051615"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1109\/TIM.2014.2317296","article-title":"Automated biosignal quality analysis for electromyography using a one-class support vector machine","volume":"63","author":"Fraser","year":"2014","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_9","unstructured":"(2019, April 23). Noraxon Ultium EMG. Available online: https:\/\/www.noraxon.com\/our-products\/ultium-emg\/#1541097779421-89a192e6-7d8d."},{"key":"ref_10","unstructured":"(2019, April 23). Delsys Trigno Avanti Sensor. Available online: https:\/\/www.delsys.com\/trigno\/research\/#trigno-avanti-sensor."},{"key":"ref_11","unstructured":"(2019, April 23). Biometrics DataLITE Wireless Surface EMG Sensor. Available online: http:\/\/www.biometricsltd.com\/wireless-sensors.htm#emg."},{"key":"ref_12","unstructured":"(2019, April 23). Thalmics Labs Myo Armband. Available online: https:\/\/support.getmyo.com\/hc\/en-us."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1109\/TNSRE.2018.2855561","article-title":"Evaluation of Myoelectric Control Learning Using Multi-Session Game-Based Training","volume":"26","author":"Tabor","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1080\/20445911.2018.1461104","article-title":"To go or not to go? Pupillometry elucidates inhibitory mechanisms in motor imagery","volume":"30","author":"Moran","year":"2018","journal-title":"J. Cogn. Psychol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Abreu, J.G., Teixeira, J.M., Figueiredo, L.S., and Teichrieb, V. (2016, January 21\u201324). Evaluating sign language recognition using the myo armband. Proceedings of the 2016 XVIII Symposium on Virtual and Augmented Reality (SVR), Gramado, Brazil.","DOI":"10.1109\/SVR.2016.21"},{"key":"ref_16","unstructured":"(2019, April 23). Oymotion G-Force pro. Available online: http:\/\/www.oymotion.com\/site\/."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1109\/TBCAS.2015.2476555","article-title":"A versatile embedded platform for EMG acquisition and gesture recognition","volume":"9","author":"Benatti","year":"2015","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"489","DOI":"10.3390\/s120100489","article-title":"A wireless sEMG recording system and its application to muscle fatigue detection","volume":"12","author":"Chang","year":"2012","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/TBCAS.2017.2757400","article-title":"An embedded, eight channel, noise canceling, wireless, wearable sEMG data acquisition system with adaptive muscle contraction detection","volume":"12","author":"Ergeneci","year":"2018","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1109\/JBHI.2016.2598302","article-title":"A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors","volume":"20","author":"Wu","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fang, Y., Zhu, X., and Liu, H. (2013, January 25\u201328). Development of a surface emg acquisition system with novel electrodes configuration and signal representation. Proceedings of the International Conference on Intelligent Robotics and Applications, Busan, Korea.","DOI":"10.1007\/978-3-642-40852-6_41"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gagnon-Turcotte, G., Ethier, C., De K\u00f6ninck, Y., and Gosselin, B. (2018, January 11\u201315). A 0.13-\u03bcm CMOS SoC for Simultaneous Multichannel Optogenetics and Electrophysiological Brain Recording. Proceedings of the IEEE International Solid-State Circuits Conference (ISSCC), San Francisco, CA, USA.","DOI":"10.1109\/ISSCC.2018.8310386"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3087","DOI":"10.1109\/JSSC.2018.2865474","article-title":"A 0.13-\u03bcm CMOS SoC for Simultaneous Multichannel Optogenetics and Neural Recording","volume":"53","author":"Khiarak","year":"2018","journal-title":"IEEE J. Solid-State Circuits"},{"key":"ref_24","unstructured":"Gagnon-Turcotte, G., Fall, C.L., Mascret, Q., Bielmann, M., Bouyer, L., and Gosselin, B. (2018, January 28\u201330). A Multichannel Wireless sEMG Sensor Endowing a 0.13 \u03bcm CMOS Mixed-Signal SoC. Proceedings of the IEEE Life Sciences Conference (LSC), Montreal, QC, Canada."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1109\/JBHI.2016.2642837","article-title":"Wireless sEMG-Based Body\u2013Machine Interface for Assistive Technology Devices","volume":"21","author":"Fall","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Madgwick, S.O., Harrison, A.J., and Vaidyanathan, R. (July, January 29). Estimation of IMU and MARG orientation using a gradient descent algorithm. Proceedings of the 2011 IEEE International Conference on Rehabilitation Robotics, Zurich, Switzerland.","DOI":"10.1109\/ICORR.2011.5975346"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Espinosa, A., Espinosa-Castillo, N., and Vald\u00e9s-Aguirre, B. (2018). Foot-Mounted Inertial Measurement Units-Based Device for Ankle Rehabilitation. Appl. Sci., 8.","DOI":"10.3390\/app8112032"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Georgi, M., Amma, C., and Schultz, T. (2015, January 12\u201315). Recognizing Hand and Finger Gestures with IMU based Motion and EMG based Muscle Activity Sensing. Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS-2015), Lisbon, Portugal.","DOI":"10.5220\/0005276900990108"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3323213","article-title":"Engaging with Robotic Swarms: Commands from Expressive Motion","volume":"8","author":"Glette","year":"2019","journal-title":"ACM Trans. Hum.-Robot Interact."},{"key":"ref_30","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 2013 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2013.6497171"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TSMCA.2011.2116004","article-title":"A framework for hand gesture recognition based on accelerometer and EMG sensors","volume":"41","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/TNSRE.2010.2100828","article-title":"Determining the optimal window length for pattern recognition-based myoelectric control: Balancing the competing effects of classification error and controller delay","volume":"19","author":"Smith","year":"2011","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1109\/TBME.2003.813539","article-title":"A robust, real-time control scheme for multifunction myoelectric control","volume":"50","author":"Englehart","year":"2003","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1682\/JRRD.2010.09.0177","article-title":"Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use","volume":"48","author":"Scheme","year":"2011","journal-title":"J. Rehabil. Res. Dev."},{"key":"ref_35","unstructured":"Phinyomark, A., Hirunviriya, S., Limsakul, C., and Phukpattaranont, P. (2010, January 19\u201321). Evaluation of EMG feature extraction for hand movement recognition based on Euclidean distance and standard deviation. Proceedings of the ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology, Chiang Mai, Thailand."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1109\/TNSRE.2019.2896269","article-title":"Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning","volume":"27","author":"Fall","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3376","DOI":"10.1109\/TII.2017.2779814","article-title":"Feasibility of Wrist-Worn, Real-Time Hand, and Surface Gesture Recognition via sEMG and IMU Sensing","volume":"14","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zia ur Rehman, M., Waris, A., Gilani, S., Jochumsen, M., Niazi, I., Jamil, M., Farina, D., and Kamavuako, E. (2018). Multiday EMG-based classification of hand motions with deep learning techniques. Sensors, 18.","DOI":"10.3390\/s18082497"},{"key":"ref_39","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cote-Allard, U., Fall, C.L., Campeau-Lecours, A., Gosselin, C., Laviolette, F., and Gosselin, B. (2017, January 5\u20138). Transfer learning for sEMG hand gestures recognition using convolutional neural networks. Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada.","DOI":"10.1109\/SMC.2017.8122854"},{"key":"ref_41","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","article-title":"Individual comparisons by ranking methods","volume":"1","author":"Wilcoxon","year":"1945","journal-title":"Biom. Bull."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Phinyomark, A., and Scheme, E. (2018). EMG pattern recognition in the era of big data and deep learning. Big Data Cogn. Comput., 2.","DOI":"10.3390\/bdcc2030021"},{"key":"ref_44","first-page":"2212","article-title":"Dry electrodes for monitoring of vital signs in functional textiles","volume":"1","author":"Muhlstell","year":"2004","journal-title":"IEEE Eng. Med. Biol. Soc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1109\/TBME.2011.2159216","article-title":"The effects of electrode size and orientation on the sensitivity of myoelectric pattern recognition systems to electrode shift","volume":"58","author":"Young","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/12\/2811\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:00:47Z","timestamp":1760187647000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/12\/2811"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,24]]},"references-count":45,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["s19122811"],"URL":"https:\/\/doi.org\/10.3390\/s19122811","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,24]]}}}