{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T21:17:00Z","timestamp":1776115020837,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,6,11]],"date-time":"2018-06-11T00:00:00Z","timestamp":1528675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>This article focuses on the use of data gloves for human-computer interaction concepts, where external sensors cannot always fully observe the user\u2019s hand. A good concept hereby allows to intuitively switch the interaction context on demand by using different hand gestures. The recognition of various, possibly complex hand gestures, however, introduces unintentional overhead to the system. Consequently, we present a data glove prototype comprising a glove-embedded gesture classifier utilizing data from Inertial Measurement Units (IMUs) in the fingertips. In an extensive set of experiments with 57 participants, our system was tested with 22 hand gestures, all taken from the French Sign Language (LSF) alphabet. Results show that our system is capable of detecting the LSF alphabet with a mean accuracy score of 92% and an F1 score of 91%, using complementary filter with a gyroscope-to-accelerometer ratio of 93%. Our approach has also been compared to the local fusion algorithm on an IMU motion sensor, showing faster settling times and less delays after gesture changes. Real-time performance of the recognition is shown to occur within 63 milliseconds, allowing fluent use of the gestures via Bluetooth-connected systems.<\/jats:p>","DOI":"10.3390\/informatics5020028","type":"journal-article","created":{"date-parts":[[2018,6,11]],"date-time":"2018-06-11T11:01:01Z","timestamp":1528714861000},"page":"28","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":94,"title":["Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove"],"prefix":"10.3390","volume":"5","author":[{"given":"Chaithanya","family":"Mummadi","sequence":"first","affiliation":[{"name":"Faculty of Engineering, University of Freiburg, 79085 Freiburg im Breisgau, Germany"}]},{"given":"Frederic","family":"Leo","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Freiburg, 79085 Freiburg im Breisgau, Germany"}]},{"given":"Keshav","family":"Verma","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Freiburg, 79085 Freiburg im Breisgau, Germany"}]},{"given":"Shivaji","family":"Kasireddy","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Freiburg, 79085 Freiburg im Breisgau, Germany"}]},{"given":"Philipp","family":"Scholl","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Freiburg, 79085 Freiburg im Breisgau, Germany"}]},{"given":"Jochen","family":"Kempfle","sequence":"additional","affiliation":[{"name":"Ubiquitous Computing, University of Siegen, 57068 Siegen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5296-5347","authenticated-orcid":false,"given":"Kristof","family":"Laerhoven","sequence":"additional","affiliation":[{"name":"Ubiquitous Computing, University of Siegen, 57068 Siegen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mummadi, C.K., Leo, F.P.P., Verma, K.D., Kasireddy, S., Scholl, P.M., and Laerhoven, K.V. (2017, January 21\u201322). Real-time Embedded Recognition of Sign Language Alphabet Fingerspelling in an IMU-Based Glove. Proceedings of the 4th international Workshop on Sensor-based Activity Recognition and Interaction\u2014iWOAR, Rostock, Germany.","DOI":"10.1145\/3134230.3134236"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1109\/TSMCC.2008.923862","article-title":"A survey of glove-based systems and their applications","volume":"38","author":"Dipietro","year":"2008","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/38.250916","article-title":"A survey of glove-based input","volume":"14","author":"Sturman","year":"1994","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zimmerman, T.G., Lanier, J., Blanchard, C., Bryson, S., and Harvill, Y. (1987, January 5\u20139). A Hand Gesture Interface Device. Proceedings of the SIGCHI\/GI Conference on Human Factors in Computing Systems and Graphics Interface, Toronto, CA, USA.","DOI":"10.1145\/29933.275628"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cabrera, M.E., Bogado, J.M., Fermin, L., Acuna, R., and Ralev, D. (2012, January 23\u201326). Glove-based gesture recognition system. Proceedings of the 15th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, Baltimore, MD, USA.","DOI":"10.1142\/9789814415958_0095"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Vutinuntakasame, S.E.A. (2011, January 23\u201325). An assistive body sensor network glove for speech-and hearing-impaired disabilities. Proceedings of the IEEE 2011 International Conference on Body Sensor Networks (BSN), Dallas, TX, USA.","DOI":"10.1109\/BSN.2011.13"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tanyawiwat, N., and Thiemjarus, S. (2012, January 9\u201312). Design of an assistive communication glove using combined sensory channels. Proceedings of the 2012 Ninth International IEEE Conference on Wearable and Implantable Body Sensor Networks (BSN), London, UK.","DOI":"10.1109\/BSN.2012.17"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hein, A., and Kirste, T. (2009). A Hybrid Approach for Recognizing ADLs and Care Activities Using Inertial Sensors and RFID. Proceedings of the 5th International on Conference Universal Access in Human-Computer Interaction, Intelligent and Ubiquitous Interaction Environments (UAHCI \u201909), San Diego, CA, USA, 19\u201324 July 2009, Springer.","DOI":"10.1007\/978-3-642-02710-9_21"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zinnen, A., van Laerhoven, K., and Schiele, B. (2007). Toward Recognition of Short and Non-repetitive Activities from Wearable Sensors. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-540-76652-0_9"},{"key":"ref_10","unstructured":"Kuroda, T., Tabata, Y., Goto, A., Ikuta, H., and Murakami, M. (2004, January 20\u201322). Consumer price data-glove for sign language recognition. Proceedings of the 5th International Conference on Disability, Virtual Reality and Associated Technologies (ICDVRAT 2004), Oxford, UK."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Khambaty, Y., Quintana, R., Shadaram, M., Nehal, S., Virk, M.A., Ahmed, W., and Ahmedani, G. (2008, January 2\u20134). Cost effective portable system for sign language gesture recognition. Proceedings of the IEEE International Conference on System of Systems Engineering (SoSE \u201908), Singapore.","DOI":"10.1109\/SYSOSE.2008.4724149"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Huang, Y., Monekosso, D., Wang, H., and Augusto, J.C. (2011, January 25\u201328). A concept grounding approach for glove-based gesture recognition. Proceedings of the 7th IEEE International Conference on Intelligent Environments (IE), Nottingham, UK.","DOI":"10.1109\/IE.2011.51"},{"key":"ref_13","unstructured":"Huang, Y., Monekosso, D., and Wang, H. (2010, January 29\u201331). Clustering ensembles based on multi-classifier fusion. Proceedings of the 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS), Xiamen, China."},{"key":"ref_14","unstructured":"Jeong, E., Lee, J., and Kim, D. (2011, January 26\u201329). Finger-gesture recognition glove using velostat (ICCAS 2011). Proceedings of the 2011 11th IEEE International Conference on Control, Automation and Systems (ICCAS), Gyeonggi-do, South Korea."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Park, Y., Lee, J., and Bae, J. (2014, January 8\u201311). Development of a finger motion measurement system using linear potentiometers. Proceedings of the 2014 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics, Besacon, France.","DOI":"10.1109\/AIM.2014.6878066"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kadam, K., Ganu, R., Bhosekar, A., and Joshi, S. (2012, January 18\u201320). American Sign Language Interpreter. Proceedings of the 2012 IEEE Fourth International Conference on Technology for Education, Hyderabad, India.","DOI":"10.1109\/T4E.2012.45"},{"key":"ref_17","unstructured":"Perng, J.K., Fisher, B.D., Hollar, S., and Pister, K.S.J. (1999, January 18\u201319). Acceleration Sensing Glove (ASG). Proceedings of the Third International Symposium on Wearable Computers (ISWC 1999), San Francisco, CA, USA."},{"key":"ref_18","unstructured":"Wu, J., Gao, W., Song, Y., Liu, W., and Pang, B. (1998, January 12\u201316). A simple sign language recognition system based on data glove. Proceedings of the 1998 Fourth IEEE International Conference on Signal Processing Proceedings (ICSP\u201998), Beijing, China."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hrabia, C.E., Wolf, K., and Wilhelm, M. (2013, January 7\u20138). Whole hand modeling using 8 wearable sensors: Biomechanics for hand pose prediction. Proceedings of the 4th Augmented Human International Conference, New York, NY, USA.","DOI":"10.1145\/2459236.2459241"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1109\/THMS.2015.2406692","article-title":"Glove-based continuous Arabic sign language recognition in user-dependent mode","volume":"45","author":"Tubaiz","year":"2015","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kim, J., Wagner, J., Rehm, M., and Andr\u00e9, E. (2008, January 17\u201319). Bi-channel sensor fusion for automatic sign language recognition. Proceedings of the 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, Amsterdam, The Netherlands.","DOI":"10.1109\/AFGR.2008.4813341"},{"key":"ref_22","unstructured":"Brashear, H., Starner, T., Lukowicz, P., and Junker, H. (2003). Using Multiple Sensors for Mobile Sign Language Recognition, IEEE Press. ISWC 2003."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2891","DOI":"10.1016\/j.neucom.2006.04.016","article-title":"Linguistic properties based on American Sign Language isolated word recognition with artificial neural networks using a sensory glove and motion tracker","volume":"70","author":"Oz","year":"2007","journal-title":"Neurocomputing"},{"key":"ref_24","first-page":"99","article-title":"Recognizing Hand and Finger Gestures with IMU Based Motion and EMG Based Muscle Activity Sensing","volume":"Volume 4","author":"Georgi","year":"2015","journal-title":"Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2015), Lisbon, Portugal, 12\u201315 January 2015"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gong, J., Yang, X.D., and Irani, P. (2016, January 16\u201319). WristWhirl: One-handed Continuous Smartwatch Input Using Wrist Gestures. Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST \u201916), Tokyo, Japan.","DOI":"10.1145\/2984511.2984563"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gui, P., Tang, L., and Mukhopadhyay, S. (2015). MEMS Based IMU for Tilting Measurement: Comparison of Complementary and Kalman Filter Based Data Fusion. Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications (ICIEA 2015), Auckland, New Zealand, 15\u201317 June 2015, IEEE.","DOI":"10.1109\/ICIEA.2015.7334442"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hammerla, N.Y., and Ploetz, T. (2015, January 7\u201311). Let\u2019s (Not) Stick Together: Pairwise Similarity Biases Cross-validation in Activity Recognition. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp \u201915), Osaka, Japan.","DOI":"10.1145\/2750858.2807551"},{"key":"ref_28","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/5\/2\/28\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:08:14Z","timestamp":1760195294000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/5\/2\/28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,11]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["informatics5020028"],"URL":"https:\/\/doi.org\/10.3390\/informatics5020028","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,11]]}}}