{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T05:56:24Z","timestamp":1775886984749,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.<\/jats:p>","DOI":"10.3390\/biomimetics8020255","type":"journal-article","created":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T02:03:19Z","timestamp":1686794599000},"page":"255","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4644-4841","authenticated-orcid":false,"given":"Karla","family":"Avil\u00e9s-Mendoza","sequence":"first","affiliation":[{"name":"Neuroimaging and Bioengineering Laboratory (LNB), Facultad de Ingenier\u00eda en Mec\u00e1nica y Ciencias de la Producci\u00f3n, Escuela Superior Polit\u00e9cnica del Litoral (ESPOL), Campus Gustavo Galindo km 30.5 V\u00eda Perimetral, Guayaquil 090903, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6827-652X","authenticated-orcid":false,"given":"Neil George","family":"Gaibor-Le\u00f3n","sequence":"additional","affiliation":[{"name":"Neuroimaging and Bioengineering Laboratory (LNB), Facultad de Ingenier\u00eda en Mec\u00e1nica y Ciencias de la Producci\u00f3n, Escuela Superior Polit\u00e9cnica del Litoral (ESPOL), Campus Gustavo Galindo km 30.5 V\u00eda Perimetral, Guayaquil 090903, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2786-4162","authenticated-orcid":false,"given":"V\u00edctor","family":"Asanza","sequence":"additional","affiliation":[{"name":"SDAS Research Group, Ben Guerir 43150, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2973-7765","authenticated-orcid":false,"given":"Leandro L.","family":"Lorente-Leyva","sequence":"additional","affiliation":[{"name":"SDAS Research Group, Ben Guerir 43150, Morocco"},{"name":"Faculty of Law, Administrative and Social Sciences, Universidad UTE, Quito 170147, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9045-6997","authenticated-orcid":false,"given":"Diego H.","family":"Peluffo-Ord\u00f3\u00f1ez","sequence":"additional","affiliation":[{"name":"SDAS Research Group, Ben Guerir 43150, Morocco"},{"name":"College of Computing, Mohammed VI Polytechnic University, Ben Guerir 47963, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1038\/s41578-018-0022-y","article-title":"Biomedical applications of soft robotics","volume":"3","author":"Cianchetti","year":"2018","journal-title":"Nat. Rev. Mater."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Amsan, A.N., Nasution, A.K., and Ramlee, M.H. (2019, January 26). A short review on the cost, design, materials and challenges of the prosthetics leg development and usage. Proceedings of the International Conference of CELSciTech 2019\u2014Science and Technology Track (ICCELST-ST 2019), Pekanbaru, Indonesia.","DOI":"10.2991\/iccelst-st-19.2019.12"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1111\/aor.13422","article-title":"Bionic hand as artificial organ: Current status and future perspectives","volume":"43","author":"Aman","year":"2019","journal-title":"Artif. Organs"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3389\/fnins.2016.00209","article-title":"Literature Review on Needs of Upper Limb Prosthesis Users","volume":"10","author":"Cordella","year":"2016","journal-title":"Front. Neurosci."},{"key":"ref_5","unstructured":"Coba, G. (2023, April 15). El Ingreso Promedio en Ecuador cae y las Horas de Trabajo Suben Ligeramente. Premicias. Available online: https:\/\/www.primicias.ec\/noticias\/economia\/ingreso-promedio-disminucion-horas-trabajo-suben\/."},{"key":"ref_6","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"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"255","DOI":"10.3109\/03091902.2016.1167971","article-title":"Recent advancements in prosthetic hand technology","volume":"40","author":"Saikia","year":"2016","journal-title":"J. Med. Eng. Technol."},{"key":"ref_8","first-page":"1167","article-title":"Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control","volume":"61","author":"Goebel","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Elbreki, A.M., Ramdan, S., Mohamed, F., Alshari, K., Rajab, Z., and Elhub, B. (2022, January 4\u20136). Practical Design of an Upper Prosthetic Limb Using Three Dimensional Printer with an Artificial Intelligence Based Controller. Proceedings of the 2022 International Conference on Engineering & MIS (ICEMIS), Istanbul, Turkey.","DOI":"10.1109\/ICEMIS56295.2022.9914291"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"10054","DOI":"10.1109\/JSEN.2023.3259150","article-title":"Implementing Hand Gesture Recognition using EMG on the Zynq Circuit","volume":"9","author":"Kerdjidj","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"38850","DOI":"10.1109\/ACCESS.2023.3267674","article-title":"Artificial Intelligence for sEMG-based Muscular Movement Recognition for Hand Prosthesis","volume":"11","author":"Hye","year":"2023","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8578","DOI":"10.1109\/JSEN.2018.2865623","article-title":"Knit band sensor for myoelectric control of surface EMG-based prosthetic hand","volume":"18","author":"Lee","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1177\/0309364617691622","article-title":"Passive prosthetic hands and tools: A literature review","volume":"42","author":"Maat","year":"2018","journal-title":"Prosthetics Orthot. Int."},{"key":"ref_14","unstructured":"Ottobock (2022, May 24). Passive Arm Prostheses: Natural-Looking Arm Prostheses. Available online: https:\/\/www.ottobockus.com\/prosthetics\/upper-limb-prosthetics\/solution-overview\/passive-arm-prostheses\/."},{"key":"ref_15","unstructured":"ArmDynamics (2022, May 27). Prosthetic Options. Available online: https:\/\/www.armdynamics.com\/our-care\/prosthetic-options."},{"key":"ref_16","unstructured":"Almao, D. (2022, May 30). Brazos Mec\u00e1nicos: Tipos, Aplicaciones, Fabricantes y los Brazos Rob\u00f3ticos M\u00e1s Avanzados del Mercado. Available online: https:\/\/www.cinconoticias.com\/brazos-mecanicos\/."},{"key":"ref_17","unstructured":"Farina, D. (2022, May 30). Retroalimentaci\u00f3n Artificial a los Usuarios de Pr\u00f3tesis. Available online: https:\/\/www.catalunyavanguardista.com\/retroalimentacion-artificial-a-los-usuarios-de-protesis\/."},{"key":"ref_18","unstructured":"De Luca, C. (2006). Encyclopedia of Medical Devices and Instrumentation, John Wiley & Sons Inc."},{"key":"ref_19","unstructured":"Zayia, L.C., and Tadi, P. (2021). StatPearls, StatPearls Publishing."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1251\/bpo115","article-title":"Techniques of EMG signal analysis: Detection, processing, classification and applications","volume":"8","author":"Reaz","year":"2006","journal-title":"Biol. Proced. Online"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ahsan, M.R., Ibrahimy, M.I., and Khalifa, O.O. (2011, January 17\u201319). Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN). Proceedings of the 2011 4th International Conference on Mechatronics (ICOM), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICOM.2011.5937135"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"576729","DOI":"10.3389\/fneur.2020.576729","article-title":"Analysis and biophysics of surface EMG for physiotherapists and kinesiologists: Toward a common language with rehabilitation engineers","volume":"11","author":"McManus","year":"2020","journal-title":"Front. Neurol."},{"key":"ref_23","unstructured":"Litin, S.C., and Nanda, S. (2019). Mayo Clinic Family Health Book, Time Incorporated Home Entertainment Des Moines."},{"key":"ref_24","unstructured":"Mahmood, D., Riaz, H.N., and Nisar, H. (2023). Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning, Springer International Publishing."},{"key":"ref_25","unstructured":"(2022, May 29). What is EMG Sensor, Myoware and How to Use with Arduino?. Available online: https:\/\/www.seeedstudio.com\/blog\/2019\/12\/29\/what-is-emg-sensor-myoware-and-how-to-use-with-arduino\/."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Farnsworth, B. (2022, May 29). What Is EMG (Electromyography) and How Does It Work?. Available online: https:\/\/imotions.com\/blog\/electromyography-101\/.","DOI":"10.1017\/9781009381055.011"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Phinyomark, A., Khushaba, R.N., and Scheme, E. (2018). Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors. Sensors, 18.","DOI":"10.3390\/s18051615"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3290","DOI":"10.1109\/TIM.2011.2159316","article-title":"A bus-based smart myoelectric electrode\/amplifier\u2014System requirements","volume":"60","author":"Wilson","year":"2011","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1097\/PRS.0000000000003745","article-title":"Hand and wrist anatomy and biomechanics: A comprehensive guide","volume":"140","author":"Chim","year":"2017","journal-title":"Plast. Reconstr. Surg."},{"key":"ref_30","unstructured":"Levangie, P.K., Norkin, C.C., and Lewek, M.D. (2019). Joint Structure and Function: A Comprehensive Analysis, F.A. Davis."},{"key":"ref_31","unstructured":"(2022, October 10). Artificial Intelligence (AI). IBM Cloud Education. Available online: https:\/\/www.ibm.com\/cloud\/learn\/what-is-artificial-intelligence."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Asanza, V., Pel\u00e1ez, E., Loayza, F., Lorente-Leyva, L.L., and Peluffo-Ord\u00f3\u00f1ez, D.H. (2022). Identification of Lower-Limb Motor Tasks via Brain\u2013Computer Interfaces: A Topical Overview. Sensors, 22.","DOI":"10.3390\/s22052028"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mambou, S.J., Maresova, P., Krejcar, O., Selamat, A., and Kuca, K. (2018). Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model. Sensors, 18.","DOI":"10.3390\/s18092799"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Daimiel Naranjo, I., Gibbs, P., Reiner, J.S., Lo Gullo, R., Sooknanan, C., Thakur, S.B., Jochelson, M.S., Sevilimedu, V., Morris, E.A., and Baltzer, P.A.T. (2021). Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis. Diagnostics, 11.","DOI":"10.3390\/diagnostics11060919"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gulum, M.A., Trombley, C.M., and Kantardzic, M. (2021). A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging. Appl. Sci., 11.","DOI":"10.3390\/app11104573"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zivkovic, M., Bacanin, N., Antonijevic, M., Nikolic, B., Kvascev, G., Marjanovic, M., and Savanovic, N. (2022). A Hybrid CNN and XGBoost Model Tuned by Modified Arithmetic Optimization Algorithm for COVID-19 Early Diagnostics from X-ray Images. Electronics, 11.","DOI":"10.3390\/electronics11223798"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Toro-Ossaba, A., Jaramillo-Tigreros, J., Tejada, J.C., Pe\u00f1a, A., L\u00f3pez-Gonz\u00e1lez, A., and Castanho, R.A. (2022). LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals. Appl. Sci., 12.","DOI":"10.3390\/app12199700"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Valdivieso Caraguay, \u00c1.L., V\u00e1sconez, J.P., Barona L\u00f3pez, L.I., and Benalc\u00e1zar, M.E. (2023). Recognition of Hand Gestures Based on EMG Signals with Deep and Double-Deep Q-Networks. Sensors, 23.","DOI":"10.3390\/s23083905"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e13109","DOI":"10.1111\/exsy.13109","article-title":"Development of intelligent and integrated technology for pattern recognition in EMG signals for robotic prosthesis command","volume":"40","author":"Xu","year":"2023","journal-title":"Expert Syst."},{"key":"ref_41","unstructured":"Emre Celebi, M. (2019). Unsupervised and Semi-Supervised Learning for Data Science, Springer Nature."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dokic, K., Martinovic, M., and Mandusic, D. (2020, January 25\u201327). Inference speed and quantisation of neural networks with TensorFlow Lite for Microcontrollers framework. Proceedings of the 2020 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Corfu, Greece.","DOI":"10.1109\/SEEDA-CECNSM49515.2020.9221846"},{"key":"ref_43","unstructured":"Tensorflow Lite (2022, August 10). IBM Cloud Education. Available online: https:\/\/www.tensorflow.org\/lite\/guide?hl=es-419."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1682\/JRRD.2011.10.0188","article-title":"Mechanical design and performance specifications of anthropomorphic prosthetic hands: A review","volume":"50","author":"Belter","year":"2013","journal-title":"J. Rehabil. Res. Dev."},{"key":"ref_45","unstructured":"Ottobock (2022, October 30). SensorHand Speed. Available online: https:\/\/shop.ottobock.us\/Prosthetics\/Upper-Limb-Prosthetics\/Myo-Hands-and-Components\/Myo-Terminal-Devices\/SensorHand-Speed\/p\/8E38~58#product-documents-section."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/LRA.2019.2956636","article-title":"OLYMPIC: A Modular, Tendon-Driven Prosthetic Hand with Novel Finger and Wrist Coupling Mechanisms","volume":"5","author":"Liow","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Pancholi, S., and Agarwal, R. (2016, January 21\u201324). Development of low cost EMG data acquisition system for Arm Activities Recognition. Proceedings of the 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India.","DOI":"10.1109\/ICACCI.2016.7732427"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.neucom.2021.10.104","article-title":"Modelling EMG driven wrist movements using a bio-inspired neural network","volume":"470","author":"Fang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.bspc.2018.08.030","article-title":"Walking gait event detection based on electromyography signals using artificial neural network","volume":"47","author":"Nazmi","year":"2019","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Toledo-P\u00e9rez, D.C., Rodr\u00edguez-Res\u00e9ndiz, J., G\u00f3mez-Loenzo, R.A., and Jauregui-Correa, J.C. (2019). Support vector machine-based EMG signal classification techniques: A review. Appl. Sci., 9.","DOI":"10.3390\/app9204402"},{"key":"ref_52","first-page":"25","article-title":"Classification of EMG signal based on time domain and frequency domain features","volume":"1","author":"Too","year":"2017","journal-title":"Int. J. Hum. Technol. Interact. (IJHaTI)"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Javaid, H.A., Rashid, N., Tiwana, M.I., and Anwar, M.W. (2018, January 7\u201311). Comparative analysis of emg signal features in time-domain and frequency-domain using myo gesture control. Proceedings of the 2018 4th International Conference on Mechatronics and Robotics Engineering, Valenciennes, France.","DOI":"10.1145\/3191477.3191495"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"101872","DOI":"10.1016\/j.bspc.2020.101872","article-title":"Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition","volume":"58","author":"Tuncer","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lima, A.A.M., Araujo, R.M., dos Santos, F.A.G., Yoshizumi, V.H., de Barros, F.K., Spatti, D.H., Liboni, L.H.B., and Dajer, M.E. (2018, January 8\u201313). Classification of hand movements from emg signals using optimized mlp. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489150"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Caesarendra, W., Lekson, S.U., Mustaqim, K.A., Winoto, A.R., and Widyotriatmo, A. (2016, January 29\u201331). A classification method of hand EMG signals based on principal component analysis and artificial neural network. Proceedings of the 2016 International Conference on Instrumentation, Control and Automation (ICA), Bandung, Indonesia.","DOI":"10.1109\/ICA.2016.7811469"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"101669","DOI":"10.1016\/j.bspc.2019.101669","article-title":"An experimental study on upper limb position invariant EMG signal classification based on deep neural network","volume":"55","author":"Mukhopadhyay","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.neucom.2020.06.139","article-title":"Deep learning for processing electromyographic signals: A taxonomy-based survey","volume":"452","author":"Buongiorno","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Hirt, B., Seyhan, H., Wagner, M., and Zumhasch, R. (2014). Anatomie und Biomechanik der Hand, Georg Thieme.","DOI":"10.1055\/b-002-101341"},{"key":"ref_60","unstructured":"Norton, R.L. (2019). Design of Machinery: An Introduction to the Synthesis and Analysis of Mechanisms and Machines, McGraw Hill."},{"key":"ref_61","first-page":"1","article-title":"Technical Features and Functionalities of Myo Armband: An Overview on Related Literature and Advanced Applications of Myoelectric Armbands Mainly Focused on Arm Prostheses","volume":"11","author":"Visconti","year":"2018","journal-title":"Int. J. Smart Sens. Intell. Syst."},{"key":"ref_62","unstructured":"Technologies, A. (2022, October 30). 3-Lead Muscle\/Electromyography Sensor for Microcontroller Applications. Available online: https:\/\/www.mouser.com\/datasheet\/2\/813\/MyowareUserManualAT-04-001-1223951.pdf."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ghazwan, A., Forrest, S.M., Holt, C.A., and Whatling, G.M. (2017). Can activities of daily living contribute to EMG normalization for gait analysis?. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0174670"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"5190","DOI":"10.1016\/j.eswa.2014.03.014","article-title":"Channel and feature selection for a surface electromyographic pattern recognition task","volume":"41","author":"Mesa","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.ifacol.2021.10.287","article-title":"SSVEP-EEG Signal Classification based on Emotiv EPOC BCI and Raspberry Pi","volume":"54","author":"Asanza","year":"2021","journal-title":"IFAC-PapersOnLine"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.ifacol.2021.10.283","article-title":"BCI System using a Novel Processing Technique Based on Electrodes Selection for Hand Prosthesis Control","volume":"54","author":"Constantine","year":"2021","journal-title":"IFAC-PapersOnLine"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Tinoco-Varela, D., Ferrer-Varela, J.A., Cruz-Morales, R.D., and Padilla-Garc\u00eda, E.A. (2022). Design and Implementation of a Prosthesis System Controlled by Electromyographic Signals Means, Characterized with Artificial Neural Networks. Micromachines, 13.","DOI":"10.3390\/mi13101681"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"360367","DOI":"10.1016\/j.jneumeth.2006.03.004","article-title":"Classification of EMG signals using wavelet neural network","volume":"156","author":"Subasi","year":"2006","journal-title":"J. Neurosci. Methods"}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/8\/2\/255\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:54:52Z","timestamp":1760126092000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/8\/2\/255"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,14]]},"references-count":68,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["biomimetics8020255"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics8020255","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,14]]}}}