{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T23:08:23Z","timestamp":1782342503541,"version":"3.54.5"},"reference-count":48,"publisher":"Association for Computing Machinery (ACM)","issue":"6","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>Early detection of myocardial infarction (MI) is essential for alleviating symptoms and improving daily activity performance. Researchers typically employ continuous segments of heartbeat signals (20\u201330 seconds), such as ECG signals, for MI detection, as MI often induces changes in heartbeat patterns. Current MI detection methods, like wearable sensors, may induce discomfort from prolonged wear, and Radio Frequency (RF) based approaches might fail to extract fine-grained heartbeat signals during vigorous movement. This article presents a reliable and motion-robust MI detection method based on RF signals. By developing a series of advanced signal processing algorithms, MI-Ra can capture fine-grained heartbeat signals during various daily activities. Our design is inspired by the fact that RF reflections caused by heartbeat signals are mixed with other motion-induced reflections in a nonlinear manner. We utilize the Taylor series expansion method to extract the linear component of these mixed non-linear signals and propose a novel Generative Adversarial Networks (GAN) method, named IQ-TransGAN, to separate the heartbeat signal. To enhance MI detection reliability, MI-Ra employs a multi-periodicity modeling method to extract refined signal representations from recovered heartbeat signals. We have recruited 50 volunteers with MI from Zhongnan Hospital of Wuhan, China, and 50 volunteers without MI, for comprehensive evaluations. The results demonstrate that MI-Ra achieves an average MI detection accuracy of 95.2% when user is quasi-stationary. Even during user non-stationary conditions, MI-Ra maintains an average detection accuracy of 90.5%. MI-Ra shows promise in paving way for smart home healthcare.<\/jats:p>","DOI":"10.1145\/3768580","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T10:35:14Z","timestamp":1758105314000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["MI-Ra: Towards Motion-robust Myocardial Infarction Detection Using Deep Wireless Sensing"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6173-2801","authenticated-orcid":false,"given":"Wu","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan Textile University - Yangguang Campus","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4055-4841","authenticated-orcid":false,"given":"Hengyu","family":"Yu","sequence":"additional","affiliation":[{"name":"Wuhan Textile University - Yangguang Campus","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2418-7351","authenticated-orcid":false,"given":"Lei","family":"Ding","sequence":"additional","affiliation":[{"name":"Wuhan Textile University - Yangguang Campus","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6563-669X","authenticated-orcid":false,"given":"Xinrong","family":"Hu","sequence":"additional","affiliation":[{"name":"Wuhan Textile University - Yangguang Campus","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3612-7989","authenticated-orcid":false,"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Wuhan University","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1382-0679","authenticated-orcid":false,"given":"Yanjiao","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9205-1881","authenticated-orcid":false,"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hong Kong University of Science and Technology","place":["Hong Kong, Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,10,25]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"2021. IWR1443BOOST. Retrieved from https:\/\/www.ti.com\/tool\/IWR1443BOOST"},{"key":"e_1_3_1_3_2","unstructured":"2024. NeuLog. 2017. Heart Rate & Pulse logger sensor NUL-208. Retrieved from https:\/\/neulog.com\/ heart-rate-pulse\/"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2017.06.027"},{"issue":"9","key":"e_1_3_1_5_2","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.1016\/j.hrthm.2021.03.044","article-title":"Validation of an algorithm for continuous monitoring of atrial fibrillation using a consumer smartwatch","volume":"18","author":"Avram Robert","year":"2021","unstructured":"Robert Avram, Mattheus Ramsis, Ashley D.Cristal, Viswam Nathan, Li Zhu, Jacob Kim, Jilong Kuang, Alex Gao, Eric Vittinghoff, Linnea Rohdin-Bibby, et\u00a0al. 2021. Validation of an algorithm for continuous monitoring of atrial fibrillation using a consumer smartwatch. Heart Rhythm 18, 9 (2021), 1482\u20131490.","journal-title":"Heart Rhythm"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bios.2015.12.083"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_22"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3483251"},{"key":"e_1_3_1_9_2","unstructured":"Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer G. Heigold S. Gelly and others. 2020. An image is worth \\(16\\times 16\\) words: Transformers for image recognition at scale. In International Conference on Learning Representations."},{"issue":"10","key":"e_1_3_1_10_2","doi-asserted-by":"crossref","first-page":"2498","DOI":"10.1109\/TMTT.2009.2029668","article-title":"Signal-to-noise ratio in Doppler radar system for heart and respiratory rate measurements","volume":"57","author":"Droitcour Amy D.","year":"2009","unstructured":"Amy D. Droitcour, Olga Boric-Lubecke, and Gregory T. A. Kovacs. 2009. Signal-to-noise ratio in Doppler radar system for heart and respiratory rate measurements. IEEE Transactions on Microwave Theory and Techniques 57, 10 (2009), 2498\u20132507.","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"key":"e_1_3_1_11_2","first-page":"4284","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Efe Ufuk","year":"2021","unstructured":"Ufuk Efe, Kutalmis Gokalp Ince, and Aydin Alatan. 2021. DFM: A performance baseline for deep feature matching. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4284\u20134293."},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419982"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419982"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.3033072"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(98)00140-3"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(98)00140-3"},{"key":"e_1_3_1_18_2","first-page":"95","volume-title":"Proceedings of the 2019 International Conference on Frontiers of Information Technology (FIT)","author":"Khan Muhammad Umar","year":"2019","unstructured":"Muhammad Umar Khan, Sumair Aziz, Asra Malik, and Muhammad Atif Imtiaz. 2019. Detection of myocardial infarction using pulse plethysmograph signals. In Proceedings of the 2019 International Conference on Frontiers of Information Technology (FIT). IEEE, 95\u2013955."},{"issue":"7","key":"e_1_3_1_19_2","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.jacc.2019.12.026","article-title":"Readmission and mortality after hospitalization for myocardial infarction and heart failure","volume":"75","author":"Ko Dennis T.","year":"2020","unstructured":"Dennis T. Ko, Rohan Khera, Geoffrey Lau, Feng Qiu, Yongfei Wang, Peter C. Austin, Maria Koh, Zhenqiu Lin, Douglas S. Lee, Harindra C. Wijeysundera, et\u00a0al. 2020. Readmission and mortality after hospitalization for myocardial infarction and heart failure. Journal of the American College of Cardiology 75, 7 (2020), 736\u2013746.","journal-title":"Journal of the American College of Cardiology"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3117811.3117839"},{"issue":"9","key":"e_1_3_1_21_2","doi-asserted-by":"crossref","first-page":"2200","DOI":"10.1038\/s41591-023-02513-2","article-title":"A new clinical classification of acute myocardial infarction","volume":"29","author":"Lindahl Bertil","year":"2023","unstructured":"Bertil Lindahl and Nicholas L. Mills. 2023. A new clinical classification of acute myocardial infarction. Nature Medicine 29, 9 (2023), 2200\u20132205.","journal-title":"Nature Medicine"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.3109\/03091900903150998"},{"issue":"2","key":"e_1_3_1_25_2","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1001\/archinte.1981.00340020026011","article-title":"Serum creatine kinase MB isoenzyme activity in long-term hemodialysis patients","volume":"141","author":"Ma King W.","year":"1981","unstructured":"King W. Ma, David C. Brown, Bernard W. Steele, and Arthur H. L. From. 1981. Serum creatine kinase MB isoenzyme activity in long-term hemodialysis patients. Archives of Internal Medicine 141, 2 (1981), 164\u2013166.","journal-title":"Archives of Internal Medicine"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.JBO.26.2.022707"},{"issue":"1","key":"e_1_3_1_27_2","first-page":"1","article-title":"Heart attack detection and medical attention using motion sensing device-kinect","volume":"4","author":"Patel Shivam","year":"2014","unstructured":"Shivam Patel and Yogesh Chauhan. 2014. Heart attack detection and medical attention using motion sensing device-kinect. International Journal of Scientific and Research Publications 4, 1 (2014), 1\u20134.","journal-title":"International Journal of Scientific and Research Publications"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102194"},{"key":"e_1_3_1_29_2","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1109\/RBME.2020.2976507","article-title":"A review on the state of the art in atrial fibrillation detection enabled by machine learning","volume":"14","author":"Rizwan Ali","year":"2020","unstructured":"Ali Rizwan, Ahmed Zoha, Ismail Ben Mabrouk, Hani M. Sabbour, Ameena Saad Al-Sumaiti, Akram Alomainy, Muhammad Ali Imran, and Qammer H. Abbasi. 2020. A review on the state of the art in atrial fibrillation detection enabled by machine learning. IEEE Reviews in Biomedical Engineering 14 (2020), 219\u2013239.","journal-title":"IEEE Reviews in Biomedical Engineering"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_1_31_2","first-page":"1","volume-title":"Proceedings of the 25th Annual International Conference on Mobile Computing and Networking","author":"Wang Anran","year":"2019","unstructured":"Anran Wang, Jacob E. Sunshine, and Shyamnath Gollakota. 2019. Contactless infant monitoring using white noise. In Proceedings of the 25th Annual International Conference on Mobile Computing and Networking. 1\u201316."},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"},{"key":"e_1_3_1_33_2","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Wu Haixu","year":"2022","unstructured":"Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. 2022. TimesNet: Temporal 2D-variation modeling for general time series analysis. In Proceedings of the 11th International Conference on Learning Representations."},{"key":"e_1_3_1_34_2","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu Haixu","year":"2021","unstructured":"Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419\u201322430.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3307334.3326074"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00024"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3214289"},{"issue":"1","key":"e_1_3_1_38_2","article-title":"Atrial fibrillation detection via contactless radio monitoring and knowledge transfer.","volume":"16","author":"Yuqin Yuan","year":"2025","unstructured":"Yuan Yuqin, Chen Jinbo, Zhang Dongheng, Geng Ruixu, Gong Hanqin, Xu Guixin, and Pu Yu. 2025. Atrial fibrillation detection via contactless radio monitoring and knowledge transfer. Nature Communications 16, 1 (2025).","journal-title":"Nature Communications"},{"issue":"18","key":"e_1_3_1_39_2","doi-asserted-by":"crossref","first-page":"7756","DOI":"10.3390\/s23187756","article-title":"Enhancing diagnosis of anterior and inferior myocardial infarctions using UWB radar and AI-driven feature fusion approach","volume":"23","author":"Zafar Kainat","year":"2023","unstructured":"Kainat Zafar, Hafeez Ur Rehman Siddiqui, Abdul Majid, Furqan Rustam, Sultan Alfarhood, Mejdl Safran, and Imran Ashraf. 2023. Enhancing diagnosis of anterior and inferior myocardial infarctions using UWB radar and AI-driven feature fusion approach. Sensors 23, 18 (2023), 7756.","journal-title":"Sensors"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3312948"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3432237"},{"key":"e_1_3_1_42_2","first-page":"7354","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zhang Han","year":"2019","unstructured":"Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. 2019. Self-attention generative adversarial networks. In Proceedings of the International Conference on Machine Learning. PMLR, 7354\u20137363."},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2020.3012681"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3146942"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3595182"},{"issue":"1","key":"e_1_3_1_46_2","article-title":"mmArrhythmia: Contactless arrhythmia detection via mmWave sensing","volume":"8","author":"Zhao Langcheng","year":"2024","unstructured":"Langcheng Zhao, Rui Lyu, Qi Lin, Anfu Zhou, Huanhuan Zhang, Huadong Ma, Jingjia Wang, Chunli Shao, and Yida Tang. 2024. mmArrhythmia: Contactless arrhythmia detection via mmWave sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, 1 (2024).","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/2973750.2973762"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485932"},{"key":"e_1_3_1_49_2","first-page":"27268","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zhou Tian","year":"2022","unstructured":"Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting. In Proceedings of the International Conference on Machine Learning. PMLR, 27268\u201327286."}],"container-title":["ACM Transactions on Sensor Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3768580","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T14:14:27Z","timestamp":1761401667000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3768580"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,25]]},"references-count":48,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11,30]]}},"alternative-id":["10.1145\/3768580"],"URL":"https:\/\/doi.org\/10.1145\/3768580","relation":{},"ISSN":["1550-4859","1550-4867"],"issn-type":[{"value":"1550-4859","type":"print"},{"value":"1550-4867","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,25]]},"assertion":[{"value":"2024-12-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-14","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}