{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:13:01Z","timestamp":1760659981643,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62401547"],"award-info":[{"award-number":["62401547"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62371441"],"award-info":[{"award-number":["62371441"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20447"],"award-info":[{"award-number":["U21A20447"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62331025"],"award-info":[{"award-number":["62331025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Cough is a key symptom reflecting respiratory health, with its frequency and pattern providing valuable insights into disease progression and clinical management. Objective and reliable cough detection systems are therefore of broad significance for healthcare and remote monitoring. However, existing algorithms often struggle to jointly model spatial and temporal information, limiting their robustness in real-world applications. To address this issue, we propose a cough recognition framework based on frequency-modulated continuous-wave (FMCW) radar, integrating a deep convolutional neural network (CNN) with a Self-Attention mechanism. The CNN extracts spatial features from range-Doppler maps, while Self-Attention captures temporal dependencies, and effective data augmentation strategies enhance generalization by simulating position variations and masking local dependencies. To rigorously evaluate practicality, we collected a large-scale radar dataset covering diverse positions, orientations, and activities. Experimental results demonstrate that, under subject-independent five-fold cross-validation, the proposed model achieved a mean F1-score of 0.974\u00b10.016 and an accuracy of 99.05\u00b10.55 %, further supported by high precision of 98.77\u00b11.05 %, recall of 96.07\u00b12.16 %, and specificity of 99.73\u00b10.23 %. These results confirm that our method is not only robust in realistic scenarios but also provides a practical pathway toward continuous, non-invasive, and privacy-preserving respiratory health monitoring in both clinical and telehealth applications.<\/jats:p>","DOI":"10.3390\/bioengineering12101112","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T07:29:38Z","timestamp":1760599778000},"page":"1112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatiotemporal Feature Learning for Daily-Life Cough Detection Using FMCW Radar"],"prefix":"10.3390","volume":"12","author":[{"given":"Saihu","family":"Lu","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7081-605X","authenticated-orcid":false,"given":"Yuhan","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangqiang","family":"He","sequence":"additional","affiliation":[{"name":"National Graduate School for Elite Engineers, Shandong University, Jinan 250100, China"},{"name":"School of Electronic Information, Aerospace Information Technology University, Jinan 250299, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongrui","family":"Bai","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing 100094, China"},{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenfeng","family":"Li","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pang","family":"Wu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3986-1540","authenticated-orcid":false,"given":"Xianxiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7581-8152","authenticated-orcid":false,"given":"Lidong","family":"Du","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0912-2624","authenticated-orcid":false,"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Fang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.resp.2018.02.005","article-title":"Is Cough Important in Acute Exacerbations of COPD?","volume":"257","author":"Crooks","year":"2018","journal-title":"Respir. Physiol. Neurobiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5207","DOI":"10.21037\/jtd-2020-icc-003","article-title":"The Present and Future of Cough Counting Tools","volume":"12","author":"Hall","year":"2020","journal-title":"J. Thorac. Dis."},{"key":"ref_3","unstructured":"Drugman, T., Urbain, J., Bauwens, N., Chessini, R., Valderrama, C., Lebecque, P., and Dutoit, T. (2019). Objective Study of Sensor Relevance for Automatic Cough Detection. arXiv."},{"key":"ref_4","unstructured":"Drugman, T., Urbain, J., Bauwens, N., Chessini, R., Aubriot, A.-S., Lebecque, P., and Dutoit, T. (2020). Audio and Contact Microphones for Cough Detection. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, J.-M., You, M., Wang, Z., Li, G.-Z., Xu, X., and Qiu, Z. (2014, January 2\u20135). Cough Detection Using Deep Neural Networks. Proceedings of the 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Belfast, UK.","DOI":"10.1109\/BIBM.2014.6999220"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Vhaduri, S., Van Kessel, T., Ko, B., Wood, D., Wang, S., and Brunschwiler, T. (2019, January 10\u201313). Nocturnal Cough and Snore Detection in Noisy Environments Using Smartphone-Microphones. Proceedings of the 2019 IEEE International Conference on Healthcare Informatics (ICHI), Xi\u2019an, China.","DOI":"10.1109\/ICHI.2019.8904563"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wahab, M., Hong, T., Molinari, K., Gauvreau, G.M., Cusack, R.P., Gao, Z., Satia, I., and Fang, Q. (2024). Automated Cough Analysis with Convolutional Recurrent Neural Network. Bioengineering, 11.","DOI":"10.3390\/bioengineering11111105"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Saeed, T., Ijaz, A., Sadiq, I., Qureshi, H.N., Rizwan, A., and Imran, A. (2024). An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio. Bioengineering, 11.","DOI":"10.3390\/bioengineering11010055"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Otoshi, T., Nagano, T., Izumi, S., Hazama, D., Katsurada, N., Yamamoto, M., Tachihara, M., Kobayashi, K., and Nishimura, Y. (2021). A Novel Automatic Cough Frequency Monitoring System Combining a Triaxial Accelerometer and a Stretchable Strain Sensor. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-89457-0"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xue, B., Shi, W., Chotirmall, S.H., Koh, V.C.A., Ang, Y.Y., Tan, R.X., and Ser, W. (2022). Distance-Based Detection of Cough, Wheeze, and Breath Sounds on Wearable Devices. Sensors, 22.","DOI":"10.3390\/s22062167"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Elfaramawy, T., Latyr Fall, C., Morissette, M., Lellouche, F., and Gosselin, B. (2017, January 25\u201328). Wireless Respiratory Monitoring and Coughing Detection Using a Wearable Patch Sensor Network. Proceedings of the 2017 15th IEEE International New Circuits and Systems Conference (NEWCAS), Strasbourg, France.","DOI":"10.1109\/NEWCAS.2017.8010139"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"17180","DOI":"10.1109\/JSEN.2021.3077530","article-title":"Contactless Small-Scale Movement Monitoring System Using Software Defined Radio for Early Diagnosis of COVID-19","volume":"21","author":"Rehman","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/RBME.2022.3156810","article-title":"Contactless WiFi Sensing and Monitoring for Future Healthcare\u2014Emerging Trends, Challenges, and Opportunities","volume":"16","author":"Ge","year":"2023","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17262","DOI":"10.1109\/JSEN.2021.3077511","article-title":"An mmWave Radar Based Real-Time Contactless Fitness Tracker Using Deep CNNs","volume":"21","author":"Tiwari","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"13","DOI":"10.26599\/TST.2023.9010037","article-title":"The Application of Artificial Intelligence in Alzheimer\u2019s Research","volume":"29","author":"Zhao","year":"2024","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_16","unstructured":"Hu, B., Lei, C., Wang, D., Zhang, S., and Chen, Z. (2019). A Preliminary Study on Data Augmentation of Deep Learning for Image Classification. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2921","DOI":"10.1109\/JSEN.2020.3028494","article-title":"A Movement Detection System Using Continuous-Wave Doppler Radar Sensor and Convolutional Neural Network to Detect Cough and Other Gestures","volume":"21","author":"Chuma","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_18","first-page":"127","article-title":"Convolutional Neural Network Model in Human Motion Detection Based on FMCW Radar Signals","volume":"3","author":"Vajs","year":"2023","journal-title":"E-Bus. Technol. Conf. Proc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4056","DOI":"10.1109\/TMTT.2022.3186014","article-title":"A Broadband MFCW Agile Radar Concept for Vital-Sign Detection Under Various Thoracic Movements","volume":"70","author":"Lin","year":"2022","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1109\/LMWC.2022.3198174","article-title":"Body Motion Artifact Cancellation Technique for Cough Detection Using FMCW Radar","volume":"33","author":"Han","year":"2023","journal-title":"IEEE Microw. Wirel. Components Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Han, K., and Hong, S. (2023, January 4\u201310). Cough Detection Using Millimeter-Wave FMCW Radar. Proceedings of the ICASSP 2023\u2014IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10095631"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"H\u00e4m\u00e4l\u00e4inen, M., Mucchi, L., Caputo, S., Biotti, L., Ciani, L., Marabissi, D., and Patrizi, G. (2021). Ultra-Wideband Radar-Based Indoor Activity Monitoring for Elderly Care. Sensors, 21.","DOI":"10.3390\/s21093158"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, H., Bo, S., Zhang, X., Wang, P., Du, L., Li, Z., Wu, P., Chen, X., Jiang, L., and Fang, Z. (2025). Event-Level Identification of Sleep Apnea Using FMCW Radar. Bioengineering, 12.","DOI":"10.3390\/bioengineering12040399"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"21038","DOI":"10.1109\/JIOT.2022.3175894","article-title":"Fall Detection System Using Millimeter-Wave Radar Based on Neural Network and Information Fusion","volume":"9","author":"Yao","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103967","DOI":"10.1016\/j.dsp.2023.103967","article-title":"Augmenting the Training Database with the Method of Gradual Similarity Ratios in the Face Recognition Systems","volume":"135","year":"2023","journal-title":"Digit. Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shijie, J., Ping, W., Peiyi, J., and Siping, H. (2017, January 20\u201322). Research on Data Augmentation for Image Classification Based on Convolution Neural Networks. Proceedings of the Proceedings of the 2017 Chinese Automation Congress (CAC), Jinan, China.","DOI":"10.1109\/CAC.2017.8243510"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"13001","DOI":"10.1609\/aaai.v34i07.7000","article-title":"Random Erasing Data Augmentation","volume":"34","author":"Zhong","year":"2020","journal-title":"AAAI"},{"key":"ref_28","unstructured":"Perez, L., Wang, J., and Mall, S. (2017). The Effectiveness of Data Augmentation in Image Classification Using Deep Learning. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3592","DOI":"10.1109\/JIOT.2020.2973272","article-title":"Deep Spatial\u2013Temporal Model Based Cross-Scene Action Recognition Using Commodity WiFi","volume":"7","author":"Sheng","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2762","DOI":"10.1109\/JIOT.2020.3022071","article-title":"SpeedNet: Indoor Speed Estimation With Radio Signals","volume":"8","author":"Chen","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_31","first-page":"57","article-title":"Facial Expression Recognition via ResNet-50","volume":"2","author":"Li","year":"2021","journal-title":"Int. J. Cognit. Comput. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Khan, R.U., Zhang, X., Kumar, R., and Aboagye, E.O. (2018, January 12\u201314). Evaluating the Performance of ResNet Model Based on Image Recognition. Proceedings of the 2018 International Conference on Computing and Artificial Intelligence, Chengdu, China.","DOI":"10.1145\/3194452.3194461"},{"key":"ref_33","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., G\u00f3mez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.26599\/TST.2023.9010126","article-title":"Detection and Diagnosis of Small Target Breast Masses Based on Convolutional Neural Networks","volume":"29","author":"Tan","year":"2024","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_35","first-page":"8024","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/10\/1112\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T08:11:52Z","timestamp":1760602312000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/10\/1112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":35,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["bioengineering12101112"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering12101112","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,15]]}}}