{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T17:12:13Z","timestamp":1787937133298,"version":"build-2784847793"},"reference-count":35,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human-to-human communication via the computer is mainly carried out using a keyboard or microphone. In the field of virtual reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g., silent commands during task-force training) or simply not possible (e.g., if the user has hearing loss). Data gloves help to increase immersion within VR, as they correspond to our natural interaction. At the same time, they offer the possibility of accurately capturing hand shapes, such as those used in non-verbal communication (e.g., thumbs up, okay gesture, \u2026) and in sign language. In this paper, we present a hand-shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an outlier detection and a feature selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial data augmentation, i.e., we created new artificial data from the recorded and filtered data to augment the training data set. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. The voting meta-classifier (VL2) proved to be the most accurate, albeit slowest, classifier. A good alternative is random forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. outlier detection was proven to be an effective approach, especially in improving the classification time. Overall, we have shown that our hand-shape recognition system using data gloves is suitable for communication within VR.<\/jats:p>","DOI":"10.3390\/s23249847","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T11:28:07Z","timestamp":1702898887000},"page":"9847","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Give Me a Sign: Using Data Gloves for Static Hand-Shape Recognition"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4948-4440","authenticated-orcid":false,"given":"Philipp","family":"Achenbach","sequence":"first","affiliation":[{"name":"Serious Games Group, Technical University of Darmstadt, 64289 Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7972-417X","authenticated-orcid":false,"given":"Sebastian","family":"Laux","sequence":"additional","affiliation":[{"name":"Serious Games Group, Technical University of Darmstadt, 64289 Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7916-0765","authenticated-orcid":false,"given":"Dennis","family":"Purdack","sequence":"additional","affiliation":[{"name":"Serious Games Group, Technical University of Darmstadt, 64289 Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9660-691X","authenticated-orcid":false,"given":"Philipp Niklas","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Serious Games Group, Technical University of Darmstadt, 64289 Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3651-8744","authenticated-orcid":false,"given":"Stefan","family":"G\u00f6bel","sequence":"additional","affiliation":[{"name":"Serious Games Group, Technical University of Darmstadt, 64289 Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2021). Deafness and Hearing Loss, World Health Organization. Technical Report."},{"key":"ref_2","unstructured":"Stokoe, W.C., Casterline, D.C., and Croneberg, C.G. (1976). A Dictionary of American Sign Language on Linguistic Principles, Linstok Press."},{"key":"ref_3","unstructured":"Stokoe, W. (1960). Sign Language Structure. 1978, Linstok Press."},{"key":"ref_4","unstructured":"Achenbach, P., G\u00f6ksu, Y., Kullmann, T., Tregel, T., and G\u00f6bel, S. (2021, January 1\u20132). Towards handshape identification for automatic gesture recognition using sign notation systems. Proceedings of the 8th European Conference on Social Media (ECSM \u201921), Online."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1093\/deafed\/enaa038","article-title":"The ASL-LEX 2.0 Project: A Database of Lexical and Phonological Properties for 2,723 Signs in American Sign Language","volume":"26","author":"Sehyr","year":"2021","journal-title":"J. Deaf Stud. Deaf Educ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"784","DOI":"10.3758\/s13428-016-0742-0","article-title":"ASL-LEX: A lexical database of American Sign Language","volume":"49","author":"Caselli","year":"2017","journal-title":"Behav. Res. Methods"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Brentari, D. (1998). A Prosodic Model of Sign Language Phonology, MIT Press. Language, Speech, and Communication.","DOI":"10.7551\/mitpress\/5644.001.0001"},{"key":"ref_8","unstructured":"Fricke, E., and Bressem, J. (2020). Gesten\u2014Gestern, Heute, \u00fcBermorgen. Vom Forschungsprojekt zur Ausstellung, Universit\u00e4tsverlag Chemnitz."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4002515","DOI":"10.1109\/TIM.2023.3243614","article-title":"State-of-the-Art in Data Gloves: A Review of Hardware, Algorithms, and Applications","volume":"72","author":"Pan","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1109\/TII.2016.2550528","article-title":"Hand Body Language Gesture Recognition Based on Signals from Specialized Glove and Machine Learning Algorithms","volume":"12","author":"Plawiak","year":"2016","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/978-3-031-15325-9_17","article-title":"Paper Beats Rock: Elaborating the Best Machine Learning Classifier for Hand Gesture Recognition","volume":"Volume 13476","author":"Spangenberger","year":"2022","journal-title":"Serious Games"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s42979-020-00396-5","article-title":"Recognition and Classification of Dynamic Hand Gestures by a Wearable Data-Glove","volume":"2","author":"Pezzuoli","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.procs.2015.12.276","article-title":"A New Data Glove Approach for Malaysian Sign Language Detection","volume":"76","author":"Shukor","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Saggio, G., Cavallo, P., Ricci, M., Errico, V., Zea, J., and Benalc\u00e1zar, M.E. (2020). Sign Language Recognition Using Wearable Electronics: Implementing k-Nearest Neighbors with Dynamic Time Warping and Convolutional Neural Network Algorithms. Sensors, 20.","DOI":"10.3390\/s20143879"},{"key":"ref_15","unstructured":"Kunz, N. (2022). Recognition and Classification of Handshapes of American Finger Alphabet. [Bachelor\u2019s Thesis, Technical University of Darmstadt]."},{"key":"ref_16","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_17","unstructured":"Ali, S., and Smith-Miles, K.A. (2006). AI 2006: Advances in Artificial Intelligence, Springer."},{"key":"ref_18","unstructured":"Ghojogh, B., and Crowley, M. (2019). The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial. arXiv."},{"key":"ref_19","first-page":"8671","article-title":"Widar3.0: Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi","volume":"44","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/3448110","article-title":"Pantomime: Mid-air gesture recognition with sparse millimeter-wave radar point clouds","volume":"5","author":"Palipana","year":"2021","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_21","unstructured":"Ohashi, H., Al-Naser, M., Ahmed, S., Akiyama, T., Sato, T., Nguyen, P., Nakamura, K., and Dengel, A. (2017, January 11). Augmenting Wearable Sensor Data with Physical Constraint for DNN-Based Human-Action Recognition. Proceedings of the Time Series Workshop, Sydney, Australia."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Um, T.T., Pfister, F.M.J., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., and Kuli\u0107, D. (2017, January 13\u201317). Data augmentation of wearable sensor data for parkinson\u2019s disease monitoring using convolutional neural networks. Proceedings of the 19th ACM International Conference on Multimodal Interaction, Glasgow, UK.","DOI":"10.1145\/3136755.3136817"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/978-3-030-11012-3_31","article-title":"A Semi-supervised Data Augmentation Approach Using 3D Graphical Engines","volume":"Volume 11130","author":"Roth","year":"2019","journal-title":"Computer Vision\u2014ECCV 2018 Workshops"},{"key":"ref_24","unstructured":"Blender Online Community (2018). Blender\u2014A 3D Modelling and Rendering Package, Stichting Blender Foundation."},{"key":"ref_25","unstructured":"Feix, T. (2011). Anthropomorphic Hand Optimization Based on a Latent Space Analysis. [Ph.D. Thesis, Technische Universit\u00e4t Wien]."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1109\/JPROC.2009.2038076","article-title":"Low-dimensional models for dimensionality reduction and signal recovery: A geometric perspective","volume":"98","author":"Baraniuk","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/BF00175354","article-title":"A genetic algorithm tutorial","volume":"4","author":"Whitley","year":"1994","journal-title":"Stat. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s11633-017-1107-6","article-title":"Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic Algorithm","volume":"15","author":"Li","year":"2018","journal-title":"Int. J. Autom. Comput."},{"key":"ref_29","unstructured":"Cunningham, P., Cord, M., and Delany, S.J. (2008). Machine Learning Techniques for Multimedia, Springer."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1016\/j.patcog.2011.01.017","article-title":"An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes","volume":"44","author":"Galar","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5358602","DOI":"10.1155\/2022\/5358602","article-title":"Binary response analysis using logistic regression in dentistry","volume":"2022","author":"Srimaneekarn","year":"2022","journal-title":"Int. J. Dent."},{"key":"ref_33","unstructured":"Zappi, P., Lombriser, C., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., and Tr\u00f6ster, G. (2008). Wireless Sensor Networks, Proceedings of the 5th European Conference, EWSN 2008, Bologna, Italy, 30 January\u20131 February 2008; Proceedings, Springer."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1017\/pds.2023.197","article-title":"From haptic interaction to design insight: An empirical comparison of commercial hand-tracking technology","volume":"3","author":"Cox","year":"2023","journal-title":"Proc. Des. Soc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9847\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:39:37Z","timestamp":1760132377000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9847"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,15]]},"references-count":35,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23249847"],"URL":"https:\/\/doi.org\/10.3390\/s23249847","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,15]]}}}