{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:59:38Z","timestamp":1760597978520,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T00:00:00Z","timestamp":1598400000000},"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>Many human activities are tactile. Recognizing how a person touches an object or a surface surrounding them is an active area of research and it has generated keen interest within the interactive surface community. In this paper, we compare two machine learning techniques, namely Artificial Neural Network (ANN) and Hidden Markov Models (HMM), as they are some of the most common techniques with low computational cost used to classify an acoustic-based input. We employ a small and low-cost hardware design composed of a microphone, a stethoscope, a conditioning circuit, and a microcontroller. Together with an appropriate surface, we integrated these components into a passive gesture recognition input system for experimental evaluation. To perform the evaluation, we acquire the signals using a small microphone and send it through the microcontroller to MATLAB\u2019s toolboxes to implement and evaluate the ANN and HMM models. We also present the hardware and software implementation and discuss the advantages and limitations of these techniques in gesture recognition while using a simple alphabet of three geometrical figures: circle, square, and triangle. The results validate the robustness of the HMM technique that achieved a success rate of 90%, with a shorter training time than the ANN.<\/jats:p>","DOI":"10.3390\/s20174803","type":"journal-article","created":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T09:05:37Z","timestamp":1598432737000},"page":"4803","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An Acoustic Sensing Gesture Recognition System Design Based on a Hidden Markov Model"],"prefix":"10.3390","volume":"20","author":[{"given":"Bruna Salles","family":"Moreira","sequence":"first","affiliation":[{"name":"Embedded Systems and Pervasive Computing Laboratory, Electrical Engineering Department, Federal University of Campina Grande, Campina Grande, Para\u00edba 58429-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7377-1258","authenticated-orcid":false,"given":"Angelo","family":"Perkusich","sequence":"additional","affiliation":[{"name":"Embedded Systems and Pervasive Computing Laboratory, Electrical Engineering Department, Federal University of Campina Grande, Campina Grande, Para\u00edba 58429-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2354-0048","authenticated-orcid":false,"given":"Saulo O. D.","family":"Luiz","sequence":"additional","affiliation":[{"name":"Embedded Systems and Pervasive Computing Laboratory, Electrical Engineering Department, Federal University of Campina Grande, Campina Grande, Para\u00edba 58429-900, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lopes, P., Jota, R., and Jorge, J.A. (2011, January 13\u201316). Augmenting touch interaction through acoustic sensing. Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces 2011, Kobe, Japan.","DOI":"10.1145\/2076354.2076364"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5285","DOI":"10.1109\/JIOT.2019.2900355","article-title":"Ubiquitous Writer: Robust Text Input for Small Mobile Devices via Acoustic Sensing","volume":"6","author":"Yin","year":"2019","journal-title":"IEEE Int. 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