{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T23:34:27Z","timestamp":1770334467627,"version":"3.49.0"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2018,12,27]],"date-time":"2018-12-27T00:00:00Z","timestamp":1545868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2018,12,27]]},"abstract":"<jats:p>American Sign Language (ASL) is widely used among hearing impaired individuals in English-speaking countries. Various technologies have been developed to perform ASL recognition, including optical signal sensing, electrical signal sensing, and mechanical signal sensing. However, wearable devices using those methods have bulky and complex sensing modules that lead to long-term discomfort as well as poor accuracy. In this paper, we present an epidermal-iontronic sensing (EIS)-based wearable device that wears on finger joints for 35 fingerspelling ASL recognitions (i.e., 26 alphabets from A to Z and 9 digits from one to nine). Compared to current on-market devices, such design is lighter, comfortable to wear and has better appearance according to user comments. When bending the finger, a physical contact forms between the ionic material and the epidermis of skin, leading to an electric double layer (EDL) established at the interface. Therefore, a significant capacitive change can be achieved with various finger gestures. By using Nafion as the ionic sensing material, we developed a sensing device to provide excellent flexibility and optical transparency. We used machine learning methods, such as neural networks to track and perform ASL recognition using the signals obtained from the designed device. The algorithm achieved a within-user accuracy of 99.6% and a cross-user accuracy of 76.1% when adapted the model to different users. This wearable device is low-cost and has broad potential to be integrated in future application of human-machine interactions (HMI), smart home controls, and nonverbal communications.<\/jats:p>","DOI":"10.1145\/3287080","type":"journal-article","created":{"date-parts":[[2018,12,27]],"date-time":"2018-12-27T19:28:03Z","timestamp":1545938883000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["EIS"],"prefix":"10.1145","volume":"2","author":[{"given":"Zijie","family":"Zhu","sequence":"first","affiliation":[{"name":"University of California, Davis, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuewei","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aakaash","family":"Kapoor","sequence":"additional","affiliation":[{"name":"University of California, Davis, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhichao","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of California, Davis, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingrui","family":"Pan","sequence":"additional","affiliation":[{"name":"University of California, Davis, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhou","family":"Yu","sequence":"additional","affiliation":[{"name":"University of California, Davis, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,12,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1093\/deafed\/enm035"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/0021-9924(85)90010-3"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1353\/sls.2006.0019"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition workshops (CVPRW '2015)","author":"Dong Cao"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2014.110"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2070481.2070532"},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the 14th International Conference on Machine Learning and Applications (ICMLA '15)","author":"Savur Celal"},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN '15)","author":"Wu Jian","year":"2015"},{"key":"e_1_2_1_9_1","first-page":"002872","volume-title":"Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC '2016)","author":"Savur Celal"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2011.06.015"},{"key":"e_1_2_1_11_1","volume-title":"Brown and Co","author":"Wilbur Ronnie","year":"1987"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.201705122"},{"key":"e_1_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Dae-Hyeong Kim Nanshu Lu Rui Ma Yun-Soung Kim Rak-Hwan Kim Shuodao Wang Jian Wu Sang Min Won Hu Tao Ahmad Islam Ki Jun Yu Tae-il Kim Raeed Chowdhury Ming Ying Lizhi Xu Ming Li Hyun-Joong Chung Hohyun Keum Martin McCormick Ping Liu Yong-Wei Zhang Fiorenzo G. 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