{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:52:35Z","timestamp":1783439555506,"version":"3.54.6"},"reference-count":72,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T00:00:00Z","timestamp":1656892800000},"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":[[2022,7,4]]},"abstract":"<jats:p>In this paper, we introduce IndexPen, a novel interaction technique for text input through two-finger in-air micro-gestures, enabling touch-free, effortless, tracking-based interaction, designed to mirror real-world writing. Our system is based on millimeter-wave radar sensing, and does not require instrumentation on the user. IndexPen can successfully identify 30 distinct gestures, representing the letters A-Z, as well as Space, Backspace, Enter, and a special Activation gesture to prevent unintentional input. Additionally, we include a noise class to differentiate gesture and non-gesture noise. We present our system design, including the radio frequency (RF) processing pipeline, classification model, and real-time detection algorithms. We further demonstrate our proof-of-concept system with data collected over ten days with five participants yielding 95.89% cross-validation accuracy on 31 classes (including noise). Moreover, we explore the learnability and adaptability of our system for real-world text input with 16 participants who are first-time users to IndexPen over five sessions. After each session, the pre-trained model from the previous five-user study is calibrated on the data collected so far for a new user through transfer learning. The F-1 score showed an average increase of 9.14% per session with the calibration, reaching an average of 88.3% on the last session across the 16 users. Meanwhile, we show that the users can type sentences with IndexPen at 86.2% accuracy, measured by string similarity. This work builds a foundation and vision for future interaction interfaces that could be enabled with this paradigm.<\/jats:p>","DOI":"10.1145\/3534601","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T18:50:18Z","timestamp":1657219818000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["IndexPen"],"prefix":"10.1145","volume":"6","author":[{"given":"Haowen","family":"Wei","sequence":"first","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziheng","family":"Li","sequence":"additional","affiliation":[{"name":"Columbia University, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander D.","family":"Galvan","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuoran","family":"Su","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaveh","family":"Pahlavan","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erin T.","family":"Solovey","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2015.7218525"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2018.2820000"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2013.2239004"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2017.150"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852239"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:CHUM.0000009225.28847.77"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","unstructured":"Edwin Chan Teddy Seyed Wolfgang Stuerzlinger Xing-Dong Yang and Frank Maurer. 2016. User Elicitation on Single-hand Microgestures. 3403--3414. https:\/\/doi.org\/10.1145\/2858036.2858589","DOI":"10.1145\/2858036.2858589"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2501988.2502016"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2858036.2858125"},{"key":"e_1_2_2_10_1","volume-title":"The 2012 international joint conference on neural networks (IJCNN)","author":"Cire\u015fan Dan C","unstructured":"Dan C Cire\u015fan, Ueli Meier, and J\u00fcrgen Schmidhuber. 2012. Transfer learning for Latin and Chinese characters with deep neural networks. In The 2012 international joint conference on neural networks (IJCNN). IEEE, 1--6."},{"key":"e_1_2_2_11_1","volume-title":"The value of online learning and MRI: finding a niche for expensive technologies. Medical teacher 36, 11","author":"Cook David A","year":"2014","unstructured":"David A Cook. 2014. The value of online learning and MRI: finding a niche for expensive technologies. Medical teacher 36, 11 (2014), 965--972."},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2019.2896269"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2642918.2647396"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aaf3f6"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2007.29"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3126594.3126615"},{"key":"e_1_2_2_17_1","volume-title":"Representation of object orientation in children: Evidence from mirror-image confusions. Visual cognition 19, 8","author":"Gregory Emma","year":"2011","unstructured":"Emma Gregory, Barbara Landau, and Michael McCloskey. 2011. Representation of object orientation in children: Evidence from mirror-image confusions. Visual cognition 19, 8 (2011), 1035--1062."},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445367"},{"key":"e_1_2_2_19_1","unstructured":"Wilbert Jan Heeringa. 2004. Measuring dialect pronunciation differences using Levenshtein distance. Ph.D. Dissertation. University Library Groningen][Host]."},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3025453.3025937"},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1080\/00224065.1986.11979014"},{"key":"e_1_2_2_22_1","volume-title":"SPYY005","author":"Iovescu Cesar","year":"2017","unstructured":"Cesar Iovescu and Sandeep Rao. 2017. The fundamentals of millimeter wave sensors. Texas Instruments, SPYY005 (2017)."},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1357054.1357089"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CSNT48778.2020.9115767"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2015.2501545"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.4898"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.3390\/s17040833"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJISTA.2008.021296"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/0031-3203(89)90036-8"},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCE46568.2020.9043082"},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925953"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3432208"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3098338"},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2010.2082539"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/LAWP.2005.852577"},{"key":"e_1_2_2_36_1","volume-title":"Peijun Zhao, Yasin Almalioglu, Pedro PB de Gusmao, Changhao Chen, Ke Sun, Niki Trigoni, and Andrew Markham.","author":"Lu Chris Xiaoxuan","year":"2020","unstructured":"Chris Xiaoxuan Lu, Muhamad Risqi U Saputra, Peijun Zhao, Yasin Almalioglu, Pedro PB de Gusmao, Changhao Chen, Ke Sun, Niki Trigoni, and Andrew Markham. 2020. milliEgo: mmWave Aided Egomotion Estimation with Deep Sensor Fusion. arXiv preprint arXiv:2006.02266 (2020)."},{"key":"e_1_2_2_37_1","first-page":"502","article-title":"Balanced debounce circuit with noise filter for digital system","volume":"8","author":"Lu Robin","year":"2013","unstructured":"Robin Lu and Yan Zhang. 2013. Balanced debounce circuit with noise filter for digital system. US Patent 8,502,593.","journal-title":"US Patent"},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3191755"},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2014.7025313"},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2015.7301342"},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2294-8"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2993548"},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448110"},{"key":"e_1_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/2500423.2500436"},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2721914.2721919"},{"key":"e_1_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2017.00-19"},{"key":"e_1_2_2_48_1","first-page":"214","article-title":"Palm pilot holder","volume":"29","author":"Richter Herbert","year":"2000","unstructured":"Herbert Richter. 2000. Palm pilot holder. US Patent App. 29\/114,214.","journal-title":"US Patent App."},{"key":"e_1_2_2_49_1","volume-title":"Tesla-Rapture: A Lightweight Gesture Recognition System from mmWave Radar Point Clouds. arXiv preprint arXiv:2109.06448","author":"Salami Dariush","year":"2021","unstructured":"Dariush Salami, Ramin Hasibi, Sameera Palipana, Petar Popovski, Tom Michoel, and Stephan Sigg. 2021. Tesla-Rapture: A Lightweight Gesture Recognition System from mmWave Radar Point Clouds. arXiv preprint arXiv:2109.06448 (2021)."},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/1357054.1357138"},{"key":"e_1_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/1622176.1622208"},{"key":"e_1_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/2935334.2935336"},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_2_2_54_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSENS.2018.2810093"},{"key":"e_1_2_2_56_1","volume-title":"Don't decay the learning rate, increase the batch size. arXiv preprint arXiv:1711.00489","author":"Smith Samuel L","year":"2017","unstructured":"Samuel L Smith, Pieter-Jan Kindermans, Chris Ying, and Quoc V Le. 2017. Don't decay the learning rate, increase the batch size. arXiv preprint arXiv:1711.00489 (2017)."},{"key":"e_1_2_2_57_1","volume-title":"Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 (2014), 1929--1958."},{"key":"e_1_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.29322\/IJSRP.9.10.2019.p9420"},{"key":"e_1_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300766"},{"key":"e_1_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/CRV.2005.39"},{"key":"e_1_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2984511.2984565"},{"key":"e_1_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.664283"},{"key":"e_1_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.3390\/s130506380"},{"key":"e_1_2_2_64_1","volume-title":"2017 2nd International Conference on Image, Vision and Computing (ICIVC). IEEE, 783--787","author":"Xia Xiaoling","year":"2017","unstructured":"Xiaoling Xia, Cui Xu, and Bing Nan. 2017. Inception-v3 for flower classification. In 2017 2nd International Conference on Image, Vision and Computing (ICIVC). IEEE, 783--787."},{"key":"e_1_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1051\/itmconf\/20171201005"},{"key":"e_1_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3332165.3347865"},{"key":"e_1_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/2984511.2984515"},{"key":"e_1_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/VTC2020-Spring48590.2020.9128573"},{"key":"e_1_2_2_69_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2013.2279680"},{"key":"e_1_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/2858036.2858082"},{"key":"e_1_2_2_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/2642918.2647380"},{"key":"e_1_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2016.2549518"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534601","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534601","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T04:30:30Z","timestamp":1752467430000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534601"}},"subtitle":["Two-Finger Text Input with Millimeter-Wave Radar"],"short-title":[],"issued":{"date-parts":[[2022,7,4]]},"references-count":72,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,7,4]]}},"alternative-id":["10.1145\/3534601"],"URL":"https:\/\/doi.org\/10.1145\/3534601","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,4]]},"assertion":[{"value":"2022-07-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}