{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:35:39Z","timestamp":1768415739666,"version":"3.49.0"},"reference-count":76,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>\n                    Atrial fibrillation (AF) is characterized by irregular electrical impulses originating in the atria, which can lead to severe complications and even death. Due to the intermittent nature of the AF, early and timely monitoring of AF is critical for patients to prevent further exacerbation of the condition. Although ambulatory ECG Holter monitors provide accurate monitoring, the high cost of these devices hinders their wider adoption. Current mobile-based AF detection systems offer a portable solution, however, these systems have various applicability issues such as being easily affected by environmental factors and requiring significant user effort. To overcome the above limitations, we present\n                    <jats:italic toggle=\"yes\">MobileAF<\/jats:italic>\n                    , a novel smartphone-based AF detection system using speakers and microphones. In order to capture minute cardiac activities, we propose a multi-channel pulse wave probing method. In addition, we enhance the signal quality by introducing a three-stage pulse wave purification pipeline. What\u2019s more, a ResNet-based network model is built to implement accurate and reliable AF detection. We collect data from 23 participants utilizing our data collection application on the smartphone. Extensive experimental results demonstrate the superior performance of our system, with 98.4% accuracy, 97.6% precision, 95.8% recall, 99.2% specificity, and 96.7% F1 score.\n                  <\/jats:p>","DOI":"10.1145\/3771548","type":"journal-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T10:57:15Z","timestamp":1762599435000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Atrial Fibrillation Detection System via Acoustic Sensing for Mobile Phones"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2825-311X","authenticated-orcid":false,"given":"Xuanyu","family":"Liu","sequence":"first","affiliation":[{"name":"Research Institute of Trustworthy Autonomous Systems, Department of Computer Science and Engineering, Southern University of Science and Technology","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3918-4922","authenticated-orcid":false,"given":"Jiao","family":"Li","sequence":"additional","affiliation":[{"name":"Research Institute of Trustworthy Autonomous Systems, Department of Computer Science and Engineering, Southern University of Science and Technology","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7550-1325","authenticated-orcid":false,"given":"Haoxian","family":"Liu","sequence":"additional","affiliation":[{"name":"Research Institute of Trustworthy Autonomous Systems, Department of Computer Science and Engineering, Southern University of Science and Technology","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4191-8290","authenticated-orcid":false,"given":"Zongqi","family":"Yang","sequence":"additional","affiliation":[{"name":"Research Institute of Trustworthy Autonomous Systems, Department of Computer Science and Engineering, Southern University of Science and Technology","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0039-4026","authenticated-orcid":false,"given":"Yi","family":"Huang","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology Hospital","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2674-0918","authenticated-orcid":false,"given":"Jin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research Institute of Trustworthy Autonomous Systems, Department of Computer Science and Engineering, Southern University of Science and Technology","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"442","volume-title":"Proceedings of the 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)","author":"Aliamiri Alireza","year":"2018","unstructured":"Alireza Aliamiri and Yichen Shen. 2018. Deep learning based atrial fibrillation detection using wearable photoplethysmography sensor. In Proceedings of the 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 442\u2013445."},{"key":"e_1_3_2_3_2","unstructured":"Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)."},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1145\/3572864.3580341","volume-title":"Proceedings of the 24th International Workshop on Mobile Computing Systems and Applications","author":"Balaji Ananta Narayanan","year":"2023","unstructured":"Ananta Narayanan Balaji, Andrea Ferlini, Fahim Kawsar, and Alessandro Montanari. 2023. Stereo-bp: Non-invasive blood pressure sensing with earables. In Proceedings of the 24th International Workshop on Mobile Computing Systems and Applications. 96\u2013102."},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-49092-2"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/BHI.2018.8333374"},{"key":"e_1_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Alberto Bonomi Fons Schipper Linda Eerikainenm Jenny Margarito Ronald Aarts Saeed Babaeizadeh Helma De Morree and Lukas Dekker. 2016. Atrial fibrillation detection using photo-plethysmography and acceleration data at the wrist. In 2016 Computing in Cardiology Conference (cinc). IEEE 277\u2013280.","DOI":"10.22489\/CinC.2016.081-339"},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Ajmal et\u00a0al. 2021. Monte carlo analysis of optical heart rate sensors in commercial wearables: The effect of skin tone and obesity on the photoplethysmography (PPG) signal. Biomedical Optics Express 12 12 (2021) 7445\u20137457.","DOI":"10.1364\/BOE.439893"},{"issue":"21","key":"e_1_3_2_9_2","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1016\/j.jacc.2018.03.003","article-title":"Smartwatch algorithm for automated detection of atrial fibrillation","volume":"71","author":"Bumgarner Joseph M.","year":"2018","unstructured":"Joseph M. Bumgarner, Cameron T. Lambert, Ayman A. Hussein, Daniel J. Cantillon, Bryan Baranowski, Kathy Wolski, Bruce D. Lindsay, Oussama M. Wazni, and Khaldoun G. Tarakji. 2018. Smartwatch algorithm for automated detection of atrial fibrillation. Journal of the American College of Cardiology 71, 21 (2018), 2381\u20132388.","journal-title":"Journal of the American College of Cardiology"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1136\/heartjnl-2016-309993"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.hrthm.2018.06.006"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2018.2818185"},{"issue":"10","key":"e_1_3_2_13_2","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1088\/1361-6579\/aa8830","article-title":"Detection of atrial fibrillation using an earlobe photoplethysmographic sensor","volume":"38","author":"Conroy Thomas","year":"2017","unstructured":"Thomas Conroy, Jairo Hernandez Guzman, Burr Hall, Gill Tsouri, and Jean-Philippe Couderc. 2017. Detection of atrial fibrillation using an earlobe photoplethysmographic sensor. Physiological Measurement 38, 10 (2017), 1906.","journal-title":"Physiological Measurement"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/aa5dd7"},{"issue":"1","key":"e_1_3_2_15_2","first-page":"1","article-title":"BayesBeat: Reliable atrial fibrillation detection from noisy photoplethysmography data","volume":"6","author":"Das Sarkar Snigdha Sarathi","year":"2022","unstructured":"Sarkar Snigdha Sarathi Das, Subangkar Karmaker Shanto, Masum Rahman, Md Saiful Islam, Atif Hasan Rahman, Mohammad M. Masud, and Mohammed Eunus Ali. 2022. BayesBeat: Reliable atrial fibrillation detection from noisy photoplethysmography data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 1 (2022), 1\u201321.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.07.014"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/aad2c0"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-018-1886-0"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3570361.3613281"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2007.04.079"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3432215"},{"key":"e_1_3_2_22_2","unstructured":"Google. 2025. Battery Historian: A tool to analyze battery consumers using Android bugreport files. Retrieved 19 November 2025 from https:\/\/github.com\/google\/battery-historian"},{"key":"e_1_3_2_23_2","unstructured":"Google. 2025. The overview of Android Jetpack Microbenchmark library. Retrieved 19 November 2025 from https:\/\/developer.android.com\/topic\/performance\/benchmarking\/microbenchmark-overview"},{"key":"e_1_3_2_24_2","unstructured":"Google. 2025. Perfetto Homepage. Retrieved 19 November 2025 from https:\/\/perfetto.dev\/"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3412382.3458258"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacasi.2021.09.004"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1111\/jce.12634"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20195683"},{"key":"e_1_3_2_29_2","doi-asserted-by":"crossref","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403212"},{"issue":"7","key":"e_1_3_2_31_2","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.tcm.2019.10.010","article-title":"How useful is the smartwatch ECG?","volume":"30","author":"Isakadze Nino","year":"2020","unstructured":"Nino Isakadze and Seth S. Martin. 2020. How useful is the smartwatch ECG? Trends in Cardiovascular Medicine 30, 7 (2020), 442\u2013448.","journal-title":"Trends in Cardiovascular Medicine"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419202"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105460"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2013.2264721"},{"key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1109\/CIC.2015.7411039","volume-title":"Proceedings of the 2015 Computing in Cardiology Conference (CinC)","author":"Koivisto Tero","year":"2015","unstructured":"Tero Koivisto, Mikko P\u00e4nk\u00e4\u00e4l\u00e4, Tero Hurnanen, Tuija Vasankari, Tuomas Kiviniemi, Antti Saraste, and Juhani Airaksinen. 2015. Automatic detection of atrial fibrillation using MEMS accelerometer. In Proceedings of the 2015 Computing in Cardiology Conference (CinC). IEEE, 829\u2013832."},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCRESAHA.120.316340"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1093\/europace\/euw125"},{"issue":"1","key":"e_1_3_2_38_2","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/JBHI.2017.2688473","article-title":"Atrial fibrillation detection via accelerometer and gyroscope of a smartphone","volume":"22","author":"Lahdenoja Olli","year":"2017","unstructured":"Olli Lahdenoja, Tero Hurnanen, Zuhair Iftikhar, Sami Nieminen, Timo Knuutila, Antti Saraste, Tuomas Kiviniemi, Tuija Vasankari, Juhani Airaksinen, Mikko P\u00e4nk\u00e4\u00e4l\u00e4, et\u00a0al. 2017. Atrial fibrillation detection via accelerometer and gyroscope of a smartphone. IEEE Journal of Biomedical and Health Informatics 22, 1 (2017), 108\u2013118.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.2999101"},{"issue":"1","key":"e_1_3_2_40_2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.ijcard.2013.01.220","article-title":"iPhone ECG application for community screening to detect silent atrial fibrillation: A novel technology to prevent stroke","volume":"165","author":"Lau Jerrett K.","year":"2013","unstructured":"Jerrett K. Lau, Nicole Lowres, Lis Neubeck, David B. Brieger, Raymond W. Sy, Connor D. Galloway, David E. Albert, and Saul B. Freedman. 2013. iPhone ECG application for community screening to detect silent atrial fibrillation: A novel technology to prevent stroke. International Journal of Cardiology 165, 1 (2013), 193\u2013194.","journal-title":"International Journal of Cardiology"},{"key":"e_1_3_2_41_2","first-page":"681","volume-title":"Proceedings of the 2016 Computing in Cardiology Conference (CinC)","author":"Lemay Mathieu","year":"2016","unstructured":"Mathieu Lemay, Sibylle Fallet, Philippe Renevey, Josep Sol\u00e0, C\u00e9lestin Leupi, Etienne Pruvot, and Jean-Marc Vesin. 2016. Wrist-located optical device for atrial fibrillation screening: A clinical study on twenty patients. In Proceedings of the 2016 Computing in Cardiology Conference (CinC). 681\u2013684."},{"key":"e_1_3_2_42_2","first-page":"327","volume-title":"Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing","author":"Li Jiao","year":"2023","unstructured":"Jiao Li, Yang Liu, Zhenjiang Li, and Jin Zhang. 2023. EarPass: Continuous user authentication with in-ear PPG. In Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing. 327\u2013332."},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2015.7248369"},{"issue":"2","key":"e_1_3_2_44_2","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1177\/1747493019897870","article-title":"Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge","volume":"16","author":"Lippi Giuseppe","year":"2020","unstructured":"Giuseppe Lippi, Fabian Sanchis-Gomar, and Gianfranco Cervellin. 2020. Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. International Journal of Stroke 16, 2 (Jan2020), 217\u2013221.","journal-title":"International Journal of Stroke"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3675094.3678488"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.STR.0000166053.83476.4a"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1111\/jce.12842"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.hrthm.2012.12.001"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2016.7591456"},{"issue":"24","key":"e_1_3_2_50_2","doi-asserted-by":"crossref","first-page":"24102","DOI":"10.1109\/JSEN.2022.3217037","article-title":"Detecting atrial fibrillation in real time based on PPG via two CNNs for quality assessment and detection","volume":"22","author":"Nguyen Duc Huy","year":"2022","unstructured":"Duc Huy Nguyen, Paul C-P Chao, Chih-Chieh Chung, Ray-Hua Horng, and Bhaskar Choubey. 2022. Detecting atrial fibrillation in real time based on PPG via two CNNs for quality assessment and detection. IEEE Sensors Journal 22, 24 (2022), 24102\u201324111.","journal-title":"IEEE Sensors Journal"},{"key":"e_1_3_2_51_2","doi-asserted-by":"crossref","DOI":"10.1201\/9781351253765","volume-title":"McDonald\u2019s Blood Flow in Arteries: Theoretical, Experimental and Clinical Principles","author":"Nichols Wilmer W.","year":"2022","unstructured":"Wilmer W. Nichols, Michael O\u2019Rourke, Elazer R. Edelman, and Charalambos Vlachopoulos. 2022. McDonald\u2019s Blood Flow in Arteries: Theoretical, Experimental and Clinical Principles. CRC Press."},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.07.021"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1177\/2047487316670255"},{"key":"e_1_3_2_54_2","unstructured":"Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga and others. 2019. Pytorch: An imperative style high-performance deep learning library. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_55_2","unstructured":"UItralyticsAssistant pderrenger glenn-jocher. 2025. THOP: PyTorch-OpCounter. Retrieved 19 November 2025 from https:\/\/github.com\/ultralytics\/thop"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.2196\/12284"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/SENSORS47125.2020.9278523"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2016.7591695"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8485978"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009823001707"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2017.8036773"},{"key":"e_1_3_2_62_2","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1109\/BioCAS.2016.7833801","volume-title":"Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS)","author":"Shan Shih-Ming","year":"2016","unstructured":"Shih-Ming Shan, Sung-Chun Tang, Pei-Wen Huang, Yu-Min Lin, Wei-Han Huang, Dar-Ming Lai, and An-Yeu Andy Wu. 2016. Reliable PPG-based algorithm in atrial fibrillation detection. In Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 340\u2013343."},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/BHI.2017.7897225"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2017-017668"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2018.8513197"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamacardio.2018.0136"},{"issue":"12","key":"e_1_3_2_67_2","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1136\/neurintsurg-2021-018534","article-title":"Radial artery diameter: A comprehensive systematic review of anatomy","volume":"14","author":"Wahood Waseem","year":"2022","unstructured":"Waseem Wahood, Sherief Ghozy, Abdulaziz Al-Abdulghani, and David F. Kallmes. 2022. Radial artery diameter: A comprehensive systematic review of anatomy. Journal of Neurointerventional Surgery 14, 12 (2022), 1274\u20131278.","journal-title":"Journal of Neurointerventional Surgery"},{"issue":"9","key":"e_1_3_2_68_2","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1038\/s41551-018-0287-x","article-title":"Monitoring of the central blood pressure waveform via a conformal ultrasonic device","volume":"2","author":"Wang Chonghe","year":"2018","unstructured":"Chonghe Wang, Xiaoshi Li, Hongjie Hu, Lin Zhang, Zhenlong Huang, Muyang Lin, Zhuorui Zhang, Zhenan Yin, Brady Huang, Hua Gong, et\u00a0al. 2018. Monitoring of the central blood pressure waveform via a conformal ultrasonic device. Nature Biomedical Engineering 2, 9 (2018), 687\u2013695.","journal-title":"Nature Biomedical Engineering"},{"issue":"44","key":"e_1_3_2_69_2","doi-asserted-by":"crossref","first-page":"eabi9283","DOI":"10.1126\/sciadv.abi9283","article-title":"Flexible doppler ultrasound device for the monitoring of blood flow velocity","volume":"7","author":"Wang Fengle","year":"2021","unstructured":"Fengle Wang, Peng Jin, Yunlu Feng, Ji Fu, Peng Wang, Xin Liu, Yingchao Zhang, Yinji Ma, Yingyun Yang, Aiming Yang, et\u00a0al. 2021. Flexible doppler ultrasound device for the monitoring of blood flow velocity. Science Advances 7, 44 (2021), eabi9283.","journal-title":"Science Advances"},{"issue":"3","key":"e_1_3_2_70_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3610871","article-title":"Knowing your heart condition anytime: User-independent ECG measurement using commercial mobile phones","volume":"7","author":"Wang Lei","year":"2023","unstructured":"Lei Wang, Xingwei Wang, Dalin Zhang, Xiaolei Ma, Yong Zhang, Haipeng Dai, Chenren Xu, Zhijun Li, and Tao Gu. 2023. Knowing your heart condition anytime: User-independent ECG measurement using commercial mobile phones. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, 3 (2023), 1\u201328.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3161188"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790113"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/3569480"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-68590-4_5"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1145\/3463503"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-020-02292-9"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1145\/3643549"}],"container-title":["ACM Transactions on Sensor Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3771548","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T12:16:29Z","timestamp":1767788189000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3771548"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,7]]},"references-count":76,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,31]]}},"alternative-id":["10.1145\/3771548"],"URL":"https:\/\/doi.org\/10.1145\/3771548","relation":{},"ISSN":["1550-4859","1550-4867"],"issn-type":[{"value":"1550-4859","type":"print"},{"value":"1550-4867","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,7]]},"assertion":[{"value":"2024-11-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}