{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T14:27:37Z","timestamp":1769524057790,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T00:00:00Z","timestamp":1763769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>This work introduces two complementary surface electromyography (sEMG) datasets for hand gesture recognition. Signals were collected from 40 healthy subjects aged 18 to 40 years, divided into two independent groups of 20 participants each. In both datasets, subjects performed five hand gestures. Most of the gestures are the same, although the exact set and the order differ slightly between datasets. For example, Dataset 2 (DS2) includes the simultaneous flexion of the thumb and index finger, which is not present in Dataset 1 (DS1). Data were recorded with three bipolar sEMG sensors placed on the dominant forearm (flexor digitorum superficialis, extensor digitorum, and flexor pollicis longus). A battery-powered acquisition system was used, with sampling rates of 1000 Hz for DS1 and 1500 Hz for DS2. DS1 contains recordings performed at a constant moderate force, while DS2 includes three force levels (low, medium, and high). Both datasets provide raw signals and pre-processed versions segmented into overlapping windows, with clear file structures and annotations, enabling feature extraction for machine learning applications. Together, they constitute a large-scale standardized sEMG resource that supports the development and benchmarking of gesture and force recognition algorithms for rehabilitation, assistive technologies, and prosthetic control.<\/jats:p>","DOI":"10.3390\/data10120194","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T10:24:17Z","timestamp":1763979857000},"page":"194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SurfaceEMG Datasets for Hand Gesture Recognition Under Constant and Three-Level Force Conditions"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3220-8082","authenticated-orcid":false,"given":"Cinthya Alejandra","family":"Z\u00fa\u00f1iga-Castillo","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Santiago de Quer\u00e9taro 76010, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0769-1228","authenticated-orcid":false,"given":"V\u00edctor Alejandro","family":"Anaya-Mosqueda","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Santiago de Quer\u00e9taro 76010, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natalia Margarita","family":"Rend\u00f3n-Caballero","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Santiago de Quer\u00e9taro 76010, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2838-4854","authenticated-orcid":false,"given":"Marcos","family":"Aviles","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Santiago de Quer\u00e9taro 76010, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1304-6791","authenticated-orcid":false,"given":"Jos\u00e9 M.","family":"\u00c1lvarez-Alvarado","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Santiago de Quer\u00e9taro 76010, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7501-5272","authenticated-orcid":false,"given":"Roberto Augusto","family":"G\u00f3mez-Loenzo","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Santiago de Quer\u00e9taro 76010, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8598-5600","authenticated-orcid":false,"given":"Juvenal","family":"Rodr\u00edguez-Res\u00e9ndiz","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Santiago de Quer\u00e9taro 76010, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kadavath, M.R.K., Nasor, M., and Imran, A. (2024). Enhanced Hand Gesture Recognition with Surface Electromyogram and Machine Learning. Sensors, 24.","DOI":"10.3390\/s24165231"},{"key":"ref_2","unstructured":"mDurance Solutions SL (2025, September 07). \u00bfQu\u00e9 es la Electromiograf\u00eda de Superficie?. Available online: https:\/\/mdurance.com\/blog\/que-es-la-electromiografia-de-superficie\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111519","DOI":"10.1016\/j.dib.2025.111519","article-title":"EMG features dataset for arm activity recognition","volume":"60","author":"Challa","year":"2025","journal-title":"Data Brief"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Toledo-P\u00e9rez, D.C., Rodr\u00edguez-Res\u00e9ndiz, J., G\u00f3mez-Loenzo, R.A., and Jauregi-Correa, J.C. (2019). Support Vector Machine-Based EMG Signal Classification Techniques: A Review. Appl. Sci., 9.","DOI":"10.3390\/app9204402"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lee, K.H., Min, J.Y., and Byun, S. (2021). Electromyogram-Based classification of hand and finger gestures using artificial neural networks. Sensors, 22.","DOI":"10.3390\/s22010225"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Aarotale, P.N., and Rattani, A. (2024, January 3\u20136). Machine Learning-based sEMG Signal Classification for Hand Gesture Recognition. Proceedings of the 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Lisbon, Portugal.","DOI":"10.1109\/BIBM62325.2024.10822133"},{"key":"ref_7","first-page":"1621","article-title":"Electromyography based hand movement classification and feature extraction using machine learning algorithms","volume":"26","author":"Ekinci","year":"2023","journal-title":"J. Polytech."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Le, H., Panhuis, M.i.H., and Alici, G. (2025). Literature survey on machine learning techniques for enhancing accuracy of myoelectric hand gesture recognition in real-world prosthetic hand control. Biomim. Intell. Robot., 5.","DOI":"10.1016\/j.birob.2025.100250"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Atzori, M., and Muller, H. (2015, January 25\u201329). The Ninapro Database: A Resource for sEMG Naturally Controlled Robotic Hand Prosthetics. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7320041"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Castro, M.C.F., Arjunan, S.P., and Kumar, D.K. (2015). Selection of suitable hand gestures for reliable myoelectric human computer interface. BioMed. Eng. OnLine, 14.","DOI":"10.1186\/s12938-015-0025-5"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Young, P.R., Hong, K., Winslow, E.J., Sagastume, G.K., Battraw, M.A., Whittle, R.S., and Schofield, J.S. (2025). The effects of limb position and grasped load on hand gesture classification using electromyography, force myography, and their combination. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0321319"},{"key":"ref_12","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 11el methodology for classifying EMG movements based on SVM and genetic algorithms. Micromachines, 13.","DOI":"10.3390\/mi13122108"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/S1050-6411(03)00080-4","article-title":"Design and responses of Butterworth and critically damped digital filters","volume":"13","author":"Robertson","year":"2003","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, J., Tang, L., and Bronlund, J.E. (2013). Surface EMG signal amplification and filtering. Int. J. Comput. Appl., 82.","DOI":"10.5120\/14079-2073"},{"key":"ref_15","unstructured":"Storr, W. (2025, November 05). Sallen and Key Filter Design for Second Order Filters. Available online: https:\/\/www.electronics-tutorials.ws\/filter\/sallen-key-filter.html."},{"key":"ref_16","unstructured":"Hermens, H.J., Merletti, R., and Freriks, B. (1996). European Activities on Surface ElectroMyoGraphy, Roessingh Research and Development b.v."},{"key":"ref_17","unstructured":"Okafor, L., and Varacallo, M. (2022). Anatomy, Shoulder and Upper Limb, Hand Flexor Digitorum Superficialis Muscle. StatPearls [Internet], StatPearls Publishing. NBK539723."},{"key":"ref_18","unstructured":"Ramage, J., and Varacallo, M. (2023). Anatomy, Shoulder and Upper Limb, Wrist Extensor Muscles. StatPearls [Internet], StatPearls Publishing. NBK534805."},{"key":"ref_19","unstructured":"Benson, D., Miao, K., and Varacallo, M. (2023). Anatomy, Shoulder and Upper Limb, Hand Flexor Pollicis Longus Muscle. StatPearls [Internet], StatPearls Publishing. NBK538490."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Karrenbach, M., Preechayasomboon, P., Sauer, P., Boe, D., and Rombokas, E. (2022). Deep learning and session-specific rapid recalibration for dynamic hand gesture recognition from EMG. Front. Bioeng. Biotechnol., 10.","DOI":"10.3389\/fbioe.2022.1034672"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"016017","DOI":"10.1088\/1741-2552\/abcc7f","article-title":"Evaluation of windowing techniques for intramuscular EMG-based diagnostic, rehabilitative and assistive devices","volume":"18","author":"Ashraf","year":"2020","journal-title":"J. Neural Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Castruita-L\u00f3pez, J.F., Aviles, M., Toledo-P\u00e9rez, D.C., Mac\u00edas-Socarr\u00e1s, I., and Rodr\u00edguez-Res\u00e9ndiz, J. (2025). Electromyography Signals in Embedded Systems: A review of Processing and Classification techniques. Biomimetics, 10.","DOI":"10.3390\/biomimetics10030166"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kaczmarek, P., Ma\u0144kowski, T., and Tomczy\u0144ski, J. (2019). PUTEMG\u2014A surface Electromyography Hand Gesture Recognition Dataset. Sensors, 19.","DOI":"10.3390\/s19163548"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e27777","DOI":"10.1016\/j.heliyon.2024.e27777","article-title":"EEG and EMG-based human-machine interface for navigation of mobility-related assistive wheelchair (MRA-W)","volume":"10","author":"Welihinda","year":"2024","journal-title":"Heliyon"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Palumbo, A., Ielpo, N., Calabrese, B., Garropoli, R., Gramigna, V., Ammendolia, A., and Marotta, N. (2024). An innovative device based on human-machine interface (hmi) for powered wheelchair control for neurodegenerative disease: A proof-of-concept. Sensors, 24.","DOI":"10.3390\/s24154774"},{"key":"ref_26","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_27","first-page":"264","article-title":"Performance Analysis of EMG Signal Classification Methods for Hand Gesture Recognition in Stroke Rehabilitation","volume":"8","author":"Winursito","year":"2024","journal-title":"Elinvo (Electron. Inform. Vocat. Educ.)"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/TNSRE.2020.3038370","article-title":"A Classification and Calibration Procedure for Gesture Specific Home-Based Therapy Exercise in Young People With Cerebral Palsy","volume":"29","author":"Macintosh","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"117785","DOI":"10.1016\/j.eswa.2022.117785","article-title":"Classification and simulation of process of linear change for grip force at different grip speeds by using supervised learning based on sEMG","volume":"206","author":"Wu","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"112235","DOI":"10.1016\/j.asoc.2024.112235","article-title":"A survey on hand gesture recognition based on surface electromyography: Fundamentals, methods, applications, challenges and future trends","volume":"166","author":"Ni","year":"2024","journal-title":"Appl. Soft Comput."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/12\/194\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T11:20:16Z","timestamp":1763983216000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/12\/194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,22]]},"references-count":31,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["data10120194"],"URL":"https:\/\/doi.org\/10.3390\/data10120194","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,22]]}}}