{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T17:47:36Z","timestamp":1792950456632,"version":"4.5.0"},"reference-count":30,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T00:00:00Z","timestamp":1745193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SDSU Big Idea","award":["22YB1200"],"award-info":[{"award-number":["22YB1200"]}]},{"name":"Korean government","award":["22YB1200"],"award-info":[{"award-number":["22YB1200"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper introduces a wearable silent text input system designed to capture text input through silent speech, without generating audible sound. The system integrates Electromyography (EMG) and piezoelectric lead zirconate titanate (PZT) sensors in a miniaturized form that can be comfortably attached to the chin, making it both comfortable to wear and esthetically pleasing. The EMG sensor records muscle activity linked to specific tongue and jaw movements, while the PZT sensor measures the minute vibrations and pressure changes in the chin skin caused by silent speech. Data from both sensors are analyzed to capture the timing and intensity of the silent speech signals, allowing the extraction of key features in both time and frequency domain. Several machine learning (ML) models, including both feature-based and non-feature-based approaches commonly used for classification tasks, are employed and compared to detect and classify subtle variations in sensor signals associated with individual alphabet letters. To evaluate and compare the ML models, EMG and PZT signals for the eight most frequently used English letters are collected across one hundred trials each. Results showed that non-feature-based models, particularly the Fea-Shot Learning with fused EMG and PZT signals, achieved the highest accuracy (95.63%) and F1-score (95.62%). The proposed system\u2019s accuracy and real-time performance make it promising for silent text input and assistive communication applications.<\/jats:p>","DOI":"10.3390\/s25082624","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T20:38:26Z","timestamp":1745267906000},"page":"2624","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Wearable Silent Text Input System Using EMG and Piezoelectric Sensors"],"prefix":"10.3390","volume":"25","author":[{"given":"John S.","family":"Kang","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, San Diego State University, San Diego, CA 92182, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0172-9369","authenticated-orcid":false,"given":"Kee S.","family":"Moon","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, San Diego State University, San Diego, CA 92182, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7473-5446","authenticated-orcid":false,"given":"Sung Q.","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, San Diego State University, San Diego, CA 92182, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Satterlee","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, San Diego State University, San Diego, CA 92182, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9681-4459","authenticated-orcid":false,"given":"Xiaowei","family":"Zuo","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, San Diego State University, San Diego, CA 92182, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46031","DOI":"10.1088\/1741-2552\/aac965","article-title":"Development of sEMG sensors and algorithms for silent speech recognition","volume":"15","author":"Meltzner","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s12652-021-03315-5","article-title":"EMG-based speech recognition using dimensionality reduction methods","volume":"14","author":"Ratnovsky","year":"2023","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Song, R., Zhang, X., Chen, X., Chen, X., Chen, X., Yang, S., and Yin, E. (2023). Decoding silent speech from high-density surface electromyographic data using transformer. Biomed. Signal Process. Control, 80.","DOI":"10.1016\/j.bspc.2022.104298"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"16005","DOI":"10.1088\/1741-2552\/abca14","article-title":"Towards optimizing electrode configurations for silent speech recognition based on high-density surface electromyography","volume":"18","author":"Zhu","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7792","DOI":"10.1109\/ACCESS.2019.2963881","article-title":"Myoelectric Interfaces and Related Applications: Current State of EMG Signal Processing\u2013A Systematic Review","volume":"8","author":"Soto","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wu, R., Lu, Z., Guan, X., Zhang, M., You, W., and Li, G. (2021, January 22\u201324). Silent Speech Recognition based on sEMG and EEG Signals. Proceedings of the 2021 China Automation Congress (CAC), Beijing, China.","DOI":"10.1109\/CAC53003.2021.9727920"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"12032","DOI":"10.1088\/1742-6596\/1438\/1\/012032","article-title":"Comparison of Classifiers for EMG based Speech Recognition","volume":"1438","author":"Srisuwan","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lee, W., Seong, J.J., Ozlu, B., Shim, B.S., Marakhimov, A., and Lee, S. (2021). Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review. Sensors, 21.","DOI":"10.3390\/s21041399"},{"key":"ref_9","unstructured":"Bu, N., Tsuji, T., Arita, J., and Ohga, M. (2005, January 17\u201318). Phoneme Classification for Speech Synthesiser using Differential EMG Signals between Muscles. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jou, S., Schultz, T., Walliczek, M., Kraft, F., and Waibel, A. (2006, January 17\u201321). Towards continuous speech recognition using surface electromyography. Proceedings of the INTERSPEECH 2006, Pittsburgh, PA, USA.","DOI":"10.21437\/Interspeech.2006-212"},{"key":"ref_11","unstructured":"Wand, M., and Schultz, T. (2011, January 26\u201329). Session-independent EMG-based Speech Recognition. Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS-2011), Rome, Italy."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wand, M., and Schultz, T. (2014, January 14\u201318). Towards real-life application of EMG-based speech recognition by using unsupervised adaptation. Proceedings of the INTERSPEECH 2014, Singapore.","DOI":"10.21437\/Interspeech.2014-301"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, Q., Ruan, T., Xu, Q., Shi, Y., Yang, B., and Liu, J. (2022, January 9\u201313). Piezoelectric Mems Unvoiced Speech-Recognition Sensor Based on Oral Airflow. Proceedings of the 2022 IEEE 35th International Conference on Micro Electro Mechanical Systems Conference (MEMS), Tokyo, Japan.","DOI":"10.1109\/MEMS51670.2022.9699830"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1904020","DOI":"10.1002\/adma.201904020","article-title":"Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing","volume":"32","author":"Jung","year":"2019","journal-title":"Adv. Mater."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"177995","DOI":"10.1109\/ACCESS.2020.3026579","article-title":"Silent Speech Interfaces for Speech Restoration: A Review","volume":"8","author":"Gomez","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.specom.2012.02.001","article-title":"Small-vocabulary speech recognition using a silent speech interface based on magnetic sensing","volume":"55","author":"Hofe","year":"2013","journal-title":"Speech Commun."},{"key":"ref_17","unstructured":"Moon, K., Lee, S.Q., and Youm, W. (2024). Interactive Health-Monitoring Platform for Wearable Wireless Sensor System. (18\/798,815), U.S. Patent."},{"key":"ref_18","unstructured":"Karunaharamoorthy, A. (2025, March 15). Suprahyoid Muscles. Kenhub. Available online: https:\/\/www.kenhub.com\/en\/library\/anatomy\/suprahyoid-muscles."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"283","DOI":"10.3758\/BF03201714","article-title":"Frequency and versatility of letters in the English language","volume":"8","author":"Solso","year":"1976","journal-title":"Behav. Res. Methods Instrum."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wu, J., Zhang, Y., Xie, L., Yan, Y., Zhang, X., Liu, S., An, X., Yin, E., and Ming, D. (2022). A novel silent speech recognition approach based on parallel inception convolutional neural network and Mel frequency spectral coefficient. Front. Neurorobot., 16.","DOI":"10.3389\/fnbot.2022.971446"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"785","DOI":"10.23919\/cje.2021.00.347","article-title":"Recursive Feature Elimination Based Feature Selection in Modulation Classification for MIMO Systems","volume":"32","author":"Zhou","year":"2023","journal-title":"Chin. J. Electron."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Moon, K.S., Kang, J.S., Lee, S.Q., Thompson, J., and Satterlee, N. (2024). Wireless Mouth Motion Recognition System Based on EEG-EMG Sensors for Severe Speech Impairments. Sensors, 24.","DOI":"10.3390\/s24134125"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1016\/j.ejor.2017.08.040","article-title":"High dimensional data classification and feature selection using support vector machines","volume":"265","author":"Ghaddar","year":"2018","journal-title":"Eur. J. Oper. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"86716","DOI":"10.1109\/ACCESS.2024.3416838","article-title":"A Survey of Decision Trees: Concepts, Algorithms, and Applications","volume":"12","author":"Mienye","year":"2024","journal-title":"IEEE Access"},{"key":"ref_26","first-page":"1","article-title":"Random Forests","volume":"7","author":"Breiman","year":"2016","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_27","unstructured":"Liashchynskyi, P., and Liashchynskyi, P. (2019). Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS. arXiv."},{"key":"ref_28","unstructured":"van der Spoel, E., Rozing, M.P., Houwing-Duistermaat, J.J., Eline Slagboom, P., Beekman, M., de Craen, A.J.M., Westendorp, R.G.J., and van Heemst, D. (2015, January 6\u201311). Siamese Neural Networks for One-Shot Image Recognition. Proceedings of the 32 nd International Conference on Machine Learning, Lille, France."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, B., Cao, Y., Lin, Y., Li, Q., Zhang, Z., Long, M., and Hu, H. (2020). Negative Margin Matters: Understanding Margin in Few-Shot Classification. Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Springer. Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics.","DOI":"10.1007\/978-3-030-58548-8_26"},{"key":"ref_30","unstructured":"Grandini, M., Bagli, E., and Visani, G. (2020). Metrics for Multi-Class Classification: An Overview. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/8\/2624\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:18:44Z","timestamp":1760030324000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/8\/2624"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,21]]},"references-count":30,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["s25082624"],"URL":"https:\/\/doi.org\/10.3390\/s25082624","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,21]]}}}