{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T21:33:26Z","timestamp":1773092006014,"version":"3.50.1"},"reference-count":15,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T00:00:00Z","timestamp":1596758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Singapore Academic Research Fund","award":["R-397-000-297-114"],"award-info":[{"award-number":["R-397-000-297-114"]}]},{"name":"National Key Research and Development Program, The Ministry of Science and Technology (MOST) of China","award":["2018YFB1307703"],"award-info":[{"award-number":["2018YFB1307703"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MTI"],"abstract":"<jats:p>Wearable devices are gaining recognition for their use as a biosensor platform. Electrical impedance tomography (EIT) is one of the sensing techniques that utilizes wearable sensors as its primary data acquisition system. It measures the impedance or resistance at the peripheral (skin) level and calculates the conductivity distribution throughout the body. Even though the technology has existed for several decades, modern-day EIT devices are still costly and bulky. The paper proposes a novel low-cost kirigami-based wearable device that has soft PEDOT: PSS electrodes for sensing skin impedances. Simulation results show that the proposed kirigami structure for the bracelet has a large deformation during actuation while experiencing relatively lower stress. The paper also presents a comparative study on a few machine learning algorithms to classify hand gestures, based on the measured skin impedance. The best classification accuracy (91.49%) was observed from the quadratic support vector machine (SVM) algorithm with 48 principal components.<\/jats:p>","DOI":"10.3390\/mti4030047","type":"journal-article","created":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T09:30:54Z","timestamp":1596792654000},"page":"47","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Comparative Study of Machine Learning Algorithms to Classify Hand Gestures from Deployable and Breathable Kirigami-Based Electrical Impedance Bracelet"],"prefix":"10.3390","volume":"4","author":[{"given":"Godwin Ponraj","family":"Joseph Vedhagiri","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin Zhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National University of Singapore, Singapore 117575, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6412-5879","authenticated-orcid":false,"given":"Kirthika","family":"Senthil Kumar","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6488-1551","authenticated-orcid":false,"given":"Hongliang","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"75","DOI":"10.3389\/fnbot.2019.00075","article-title":"Finger Angle estimation from Array EMG system using linear regression model with Independent Component Analysis","volume":"13","author":"Stapornchaisit","year":"2019","journal-title":"Front. Neurorobot."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yu, M., Li, G., Jiang, D., Jiang, G., Tao, B., and Chen, D. (2019). Hand medical monitoring system based on machine learning and optimal EMG feature set. Pers. Ubiquitous Comput., 1\u201317.","DOI":"10.1007\/s00779-019-01285-2"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Harrison, C. (2015, January 8\u201311). Tomo: Wearable, low-cost electrical impedance tomography for hand gesture recognition. Proceedings of the 28th Annual ACM Symposium on User Interface Software Technology, Charlotte, NC, USA.","DOI":"10.1145\/2807442.2807480"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Xiao, R., and Harrison, C. (2016, January 16\u201319). Advancing hand gesture recognition with high resolution electrical impedance tomography. 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Mechatron., under revision."}],"container-title":["Multimodal Technologies and Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2414-4088\/4\/3\/47\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:57:42Z","timestamp":1760176662000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2414-4088\/4\/3\/47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,7]]},"references-count":15,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["mti4030047"],"URL":"https:\/\/doi.org\/10.3390\/mti4030047","relation":{},"ISSN":["2414-4088"],"issn-type":[{"value":"2414-4088","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,7]]}}}