{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T01:57:26Z","timestamp":1781315846952,"version":"3.54.1"},"reference-count":61,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T00:00:00Z","timestamp":1605139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["PID2019-104818RB-I00"],"award-info":[{"award-number":["PID2019-104818RB-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years the advances in Artificial Intelligence (AI) have been seen to play an important role in human well-being, in particular enabling novel forms of human-computer interaction for people with a disability. In this paper, we propose a sEMG-controlled 3D game that leverages a deep learning-based architecture for real-time gesture recognition. The 3D game experience developed in the study is focused on rehabilitation exercises, allowing individuals with certain disabilities to use low-cost sEMG sensors to control the game experience. For this purpose, we acquired a novel dataset of seven gestures using the Myo armband device, which we utilized to train the proposed deep learning model. The signals captured were used as an input of a Conv-GRU architecture to classify the gestures. Further, we ran a live system with the participation of different individuals and analyzed the neural network\u2019s classification for hand gestures. Finally, we also evaluated our system, testing it for 20 rounds with new participants and analyzed its results in a user study.<\/jats:p>","DOI":"10.3390\/s20226451","type":"journal-article","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T10:00:32Z","timestamp":1605175232000},"page":"6451","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["An sEMG-Controlled 3D Game for Rehabilitation Therapies: Real-Time Time Hand Gesture Recognition Using Deep Learning Techniques"],"prefix":"10.3390","volume":"20","author":[{"given":"Nadia","family":"Nasri","sequence":"first","affiliation":[{"name":"University Institute for Computer Research, University of Alicante, P.O. Box 99, 03080 Alicante, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergio","family":"Orts-Escolano","sequence":"additional","affiliation":[{"name":"University Institute for Computer Research, University of Alicante, P.O. Box 99, 03080 Alicante, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6805-3633","authenticated-orcid":false,"given":"Miguel","family":"Cazorla","sequence":"additional","affiliation":[{"name":"University Institute for Computer Research, University of Alicante, P.O. 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(2013, January 16\u201317). Punching ducks for post-stroke neurorehabilitation: System design and initial exploratory feasibility study. Proceedings of the 2013 IEEE Symposium on 3D User Interfaces (3DUI), Orlando, FL, USA.","DOI":"10.1109\/3DUI.2013.6550196"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Barresi, G., Mazzanti, D., Caldwell, D., and Brogni, A. (2013, January 3\u20135). Distractive User Interface for Repetitive Motor Tasks: A Pilot Study. Proceedings of the 2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems, Taichung, Taiwan.","DOI":"10.1109\/CISIS.2013.106"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1016\/j.compbiomed.2013.08.026","article-title":"Overall design and implementation of the virtual glove","volume":"43","author":"Placidi","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.cmpb.2016.07.014","article-title":"MirrARbilitation: A clinically-related gesture recognition interactive tool for an AR rehabilitation system","volume":"135","author":"Gama","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Brokaw, E.B., Lum, P.S., Cooper, R.A., and Brewer, B.R. (2013, January 24\u201326). Using the kinect to limit abnormal kinematics and compensation strategies during therapy with end effector robots. Proceedings of the 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR), Seattle, WA, USA.","DOI":"10.1109\/ICORR.2013.6650384"},{"key":"ref_8","unstructured":"Rado, D., Sankaran, A., Plasek, J.M., Nuckley, D.J., and Keefe, D.F. (2009, January 11\u201316). Poster: A Real-Time Physical Therapy Visualization Strategy to Improve Unsupervised Patient Rehabilitation. Proceedings of the IEEE Visualization, Atlantic City, NJ, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Khademi, M., Hondori, H.M., Dodakian, L., Cramer, S., and Lopes, C.V. (2013, January 3\u20137). Comparing \u201cpick and place\u201d task in spatial Augmented Reality versus non-immersive Virtual Reality for rehabilitation setting. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6610575"},{"key":"ref_10","unstructured":"Liao, Y., Vakanski, A., and Xian, M. (2019). A deep learning framework for assessment of quality of rehabilitation exercises. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1109\/TNSRE.2020.2966249","article-title":"A Deep Learning Framework for Assessing Physical Rehabilitation Exercises","volume":"28","author":"Liao","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1212\/WNL.0000000000005603","article-title":"Advantages of virtual reality in the rehabilitation of balance and gait","volume":"90","author":"Siemonsma","year":"2018","journal-title":"Neurology"},{"key":"ref_13","first-page":"4186","article-title":"Virtual Reality Rehabilitation Versus Conventional Physical Therapy for Improving Balance and Gait in Parkinson\u2019s Disease Patients: A Randomized Controlled Trial","volume":"25","author":"Feng","year":"2019","journal-title":"Med. Sci. Monit. Int. Med. J. Exp. Clin. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1177\/036354658201000103","article-title":"Patellar pain and quadriceps rehabilitation: An EMG study","volume":"10","author":"John","year":"1982","journal-title":"Am. J. Sport. Med."},{"key":"ref_15","unstructured":"Mulas, M., Folgheraiter, M., and Gini, G. (July, January 28). An EMG-controlled exoskeleton for hand rehabilitation. Proceedings of the 9th International Conference on Rehabilitation Robotics, Chicago, IL, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sarasola-Sanz, A., Irastorza-Landa, N., L\u00f3pez-Larraz, E., Bibi\u00e1n, C., Helmhold, F., Broetz, D., Birbaumer, N., and Ramos-Murguialday, A. (2017, January 17\u201320). A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients. Proceedings of the 2017 International Conference on Rehabilitation Robotics (ICORR), London, UK.","DOI":"10.1109\/ICORR.2017.8009362"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1109\/TNSRE.2016.2560906","article-title":"Development of an EMG-ACC-Based Upper Limb Rehabilitation Training System","volume":"25","author":"Liu","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1161\/01.STR.0000048149.84268.07","article-title":"Poststroke depression: An 18-month follow-up","volume":"34","author":"Berg","year":"2003","journal-title":"Stroke"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1159\/000047714","article-title":"Post-stroke depression, antidepressant treatment and rehabilitation results. A case-control study","volume":"12","author":"Paolucci","year":"2001","journal-title":"Cerebrovasc. Dis."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rincon, A.L., Yamasaki, H., and Shimoda, S. (2016, January 24\u201326). Design of a video game for rehabilitation using motion capture, EMG analysis and virtual reality. Proceedings of the 2016 International Conference on Electronics, Communications and Computers (CONIELECOMP), Cholula, Mexico.","DOI":"10.1109\/CONIELECOMP.2016.7438575"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12984-016-0173-2","article-title":"Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: A within-subject analysis","volume":"13","author":"Vourvopoulos","year":"2016","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1186\/s12984-017-0328-9","article-title":"Increasing upper limb training intensity in chronic stroke using embodied virtual reality: A pilot study","volume":"14","author":"Chevalley","year":"2017","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"29","DOI":"10.3233\/NRE-2009-0497","article-title":"Sensorimotor training in virtual reality: A review","volume":"25","author":"Adamovich","year":"2009","journal-title":"NeuroRehabilitation"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4193","DOI":"10.3390\/s150204193","article-title":"Wearable Sensor-Based Rehabilitation Exercise Assessment for Knee Osteoarthritis","volume":"15","author":"Chen","year":"2015","journal-title":"Sensors"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Saito, H., Watanabe, T., and Arifin, A. (2009, January 7\u201312). Ankle and Knee Joint Angle Measurements during Gait with Wearable Sensor System for Rehabilitation. Proceedings of the World Congress on Medical Physics and Biomedical Engineering, Munich, Germany.","DOI":"10.1007\/978-3-642-03889-1_134"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/1743-0003-2-2","article-title":"Advances in wearable technology and applications in physical medicine and rehabilitation","volume":"2","author":"Bonato","year":"2005","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/1743-0003-9-21","article-title":"A review of wearable sensors and systems with application in rehabilitation","volume":"9","author":"Patel","year":"2011","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/TOH.2015.2417570","article-title":"An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation","volume":"8","author":"Leonardis","year":"2015","journal-title":"IEEE Trans. Haptics"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.procs.2015.12.275","article-title":"Development of an Upper Limb Exoskeleton for Rehabilitation with Feedback from EMG and IMU Sensor","volume":"76","author":"Ganesan","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_30","first-page":"341","article-title":"Design and Development of 3D Printed Myoelectric Robotic Exoskeleton for Hand Rehabilitation","volume":"10","author":"Abdallah","year":"2017","journal-title":"Int. J. Smart Sens. Intell. Syst."},{"key":"ref_31","unstructured":"Kim, J., Bee, N., Wagner, J., and Andr\u00e9, E. (2004). Emote to Win: Affective Interactions with a Computer Game Agent, Gesellschaft fur Informatik e.V."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kim, J., Mastnik, S., and Andr\u00e9, E. (2008, January 13\u201316). EMG-based hand gesture recognition for realtime biosignal interfacing. Proceedings of the 13th International Conference on Intelligent User Interfaces, Gran Canaria, Spain.","DOI":"10.1145\/1378773.1378778"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6343","DOI":"10.1007\/s00521-019-04142-8","article-title":"Surface EMG hand gesture recognition system based on PCA and GRNN","volume":"32","author":"Qi","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.bbe.2017.11.001","article-title":"A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study","volume":"38","author":"Shi","year":"2018","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1080\/01691864.2020.1713886","article-title":"Depth vision guided hand gesture recognition using electromyographic signals","volume":"34","author":"Su","year":"2020","journal-title":"Adv. Robot."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"36571","DOI":"10.1038\/srep36571","article-title":"Gesture recognition by instantaneous surface EMG images","volume":"6","author":"Geng","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Amma, C., Krings, T., B\u00f6er, J., and Schultz, T. (2015, January 18\u201323). Advancing Muscle-Computer Interfaces with High-Density Electromyography. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea.","DOI":"10.1145\/2702123.2702501"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Atzori, M., Gijsberts, A., Heynen, S., Hager, A.G.M., Deriaz, O., van der Smagt, P., Castellini, C., Caputo, B., and Muller, H. (2012, January 24\u201327). Building the Ninapro database: A resource for the biorobotics community. Proceedings of the 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), Rome, Italy.","DOI":"10.1109\/BioRob.2012.6290287"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","unstructured":"Nasri, N., Orts, S., Gomez-Donoso, F., and Cazorla, M. (2019). Inferring Static Hand Poses from a Low-Cost Non-Intrusive sEMG Sensor. Sensors, 19.","DOI":"10.3390\/s19020371"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Nasri, N., Gomez-Donoso, F., Orts, S., and Cazorla, M. (2019, January 12\u201314). Using Inferred Gestures from sEMG Signal to Teleoperate a Domestic Robot for the Disabled. Proceedings of the IWANN, Gran Canaria, Spain.","DOI":"10.1007\/978-3-030-20518-8_17"},{"key":"ref_42","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_43","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_44","doi-asserted-by":"crossref","unstructured":"Du, Y., Wong, Y., Jin, W., Wei, W., Hu, Y., Kankanhalli, M.S., and Geng, W. (2017, January 19\u201325). Semi-Supervised Learning for Surface EMG-based Gesture Recognition. Proceedings of the IJCAI, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/225"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, G., Zhang, L., Han, B., Zhang, T., Wang, Z., and Wei, P. (2019, January 22\u201326). sEMG-Based Continuous Estimation of Knee Joint Angle Using Deep Learning with Convolutional Neural Network. Proceedings of the 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), Vancouver, BC, Canada.","DOI":"10.1109\/COASE.2019.8843168"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, J., Dai, J., Chen, S., Xu, G., and Gao, X. (2019, January 8\u201311). Design of Finger Exoskeleton Rehabilitation Robot Using the Flexible Joint and the MYO Armband. Proceedings of the ICIRA, Shenyang, China.","DOI":"10.1007\/978-3-030-27529-7_19"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Longo, B., Sime, M.M., and Bastos-Filho, T. (2019, January 21\u201325). Serious Game Based on Myo Armband for Upper-Limb Rehabilitation Exercises. Proceedings of the XXVI Brazilian Congress on Biomedical Engineering, Armacao de Buzios, Brazil.","DOI":"10.1007\/978-981-13-2119-1_107"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Widodo, M.S., Zikky, M., and Nurindiyani, A.K. (2018, January 29\u201330). Guide Gesture Application of Hand Exercises for Post-Stroke Rehabilitation Using Myo Armband. Proceedings of the 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), Bali, Indonesia.","DOI":"10.1109\/KCIC.2018.8628527"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., G\u00fcl\u00e7ehre, \u00c7., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014, January 25\u201329). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of the EMNLP, Doha, Qatar.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_50","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv."},{"key":"ref_51","unstructured":"Sutskever, I., Vinyals, O., and Le, Q.V. (2014). Sequence to Sequence Learning with Neural Networks. arXiv."},{"key":"ref_52","unstructured":"Chung, J., G\u00fcl\u00e7ehre, \u00c7., Cho, K., and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv."},{"key":"ref_53","unstructured":"Ballas, N., Yao, L., Pal, C.J., and Courville, A.C. (2016). Delving Deeper into Convolutional Networks for Learning Video Representations. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"7587","DOI":"10.1109\/ACCESS.2019.2962505","article-title":"Video Deblurring via Temporally and Spatially Variant Recurrent Neural Network","volume":"8","author":"Jiang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/LGRS.2019.2926776","article-title":"A Generative Adversarial Gated Recurrent Unit Model for Precipitation Nowcasting","volume":"17","author":"Tian","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Asadi-Aghbolaghi, M., Clapes, A., Bellantonio, M., Escalante, H.J., Ponce-L\u00f3pez, V., Bar\u00f3, X., Guyon, I., Kasaei, S., and Escalera, S. (June, January 30). A survey on deep learning based approaches for action and gesture recognition in image sequences. Proceedings of the 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, DC, USA.","DOI":"10.1109\/FG.2017.150"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Devineau, G., Moutarde, F., Xi, W., and Yang, J. (2018, January 15\u201319). Deep learning for hand gesture recognition on skeletal data. Proceedings of the 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00025"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2964","DOI":"10.1109\/TBME.2019.2899222","article-title":"Surface-Electromyography-Based Gesture Recognition by Multi-View Deep Learning","volume":"66","author":"Wei","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"9","DOI":"10.3389\/fnbot.2016.00009","article-title":"Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands","volume":"10","author":"Atzori","year":"2016","journal-title":"Front. Neurorobotics"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1007\/s12559-016-9388-6","article-title":"Sequentially supervised long short-term memory for gesture recognition","volume":"8","author":"Wang","year":"2016","journal-title":"Cogn. Comput."},{"key":"ref_61","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/22\/6451\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:32:19Z","timestamp":1760178739000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/22\/6451"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,12]]},"references-count":61,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20226451"],"URL":"https:\/\/doi.org\/10.3390\/s20226451","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,12]]}}}