{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T06:30:32Z","timestamp":1775025032650,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,16]],"date-time":"2019-11-16T00:00:00Z","timestamp":1573862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministerio de Econom\u00eda, Industria y Competitividad","award":["TIN2017- 88841-R"],"award-info":[{"award-number":["TIN2017- 88841-R"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper aims to design and implement a system capable of distinguishing between different activities carried out during a tennis match. The goal is to achieve the correct classification of a set of tennis strokes. The system must exhibit robustness to the variability of the height, age or sex of any subject that performs the actions. A new database is developed to meet this objective. The system is based on two sensor nodes using Bluetooth Low Energy (BLE) wireless technology to communicate with a PC that acts as a central device to collect the information received by the sensors. The data provided by these sensors are processed to calculate their spectrograms. Through the application of innovative deep learning techniques with semi-supervised training, it is possible to carry out the extraction of characteristics and the classification of activities. Preliminary results obtained with a data set of eight players, four women and four men have shown that our approach is able to address the problem of the diversity of human constitutions, weight and sex of different players, providing accuracy greater than 96.5% to recognize the tennis strokes of a new player never seen before by the system.<\/jats:p>","DOI":"10.3390\/s19225004","type":"journal-article","created":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T04:31:10Z","timestamp":1574051470000},"page":"5004","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Detection of Tennis Activities with Wearable Sensors"],"prefix":"10.3390","volume":"19","author":[{"given":"Luis","family":"Benages Pardo","sequence":"first","affiliation":[{"name":"Aragon Institute of Engineering Research, University of Zaragoza, Mariano Esquillor, 50018 Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Buldain Perez","sequence":"additional","affiliation":[{"name":"Aragon Institute of Engineering Research, University of Zaragoza, Mariano Esquillor, 50018 Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos","family":"Orrite Uru\u00f1uela","sequence":"additional","affiliation":[{"name":"Aragon Institute of Engineering Research, University of Zaragoza, Mariano Esquillor, 50018 Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yang, Z., and Dong, T. 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