{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:51:59Z","timestamp":1787673119722,"version":"build-2736575974"},"reference-count":29,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T00:00:00Z","timestamp":1748563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Progressive global aging has increased the number of elderly individuals living alone. The consequent rise in fall accidents has worsened physical injuries, reduced the quality of life, and increased medical expenses. Existing wearable fall-detection devices may cause discomfort, and camera-based systems raise privacy concerns. Here, we propose a non-contact fall-detection system that integrates 4D imaging radar sensors with artificial intelligence (AI) technology to detect falls through real-time monitoring and visualization using a web-based dashboard and Unity engine-based avatar, along with immediate alerts. The system eliminates the need for uncomfortable wearable devices and mitigates the privacy issues associated with cameras. The radar sensors generate Point Cloud data (the spatial coordinates, velocity, Doppler power, and time), which allow analysis of the body position and movement. A CNN model classifies postures into standing, sitting, and lying, while changes in the speed and position distinguish falling actions from lying-down actions. The Point Cloud data were normalized and organized using zero padding and k-means clustering to improve the learning efficiency. The model achieved 98.66% accuracy in posture classification and 95% in fall detection. This study demonstrates the effectiveness of the proposed fall detection approach and suggests future directions in multi-sensor integration for indoor applications.<\/jats:p>","DOI":"10.3390\/s25113452","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T08:07:58Z","timestamp":1748592478000},"page":"3452","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Non-Contact Fall Detection System Using 4D Imaging Radar for Elderly Safety Based on a CNN Model"],"prefix":"10.3390","volume":"25","author":[{"given":"Sejong","family":"Ahn","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Tech University of Korea, Siheung 15073, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6166-3614","authenticated-orcid":false,"given":"Museong","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Bio Health Convergence Engineering, Tech University of Korea, Siheung 15073, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jongjin","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Bio Health Convergence Engineering, Tech University of Korea, Siheung 15073, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinseok","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Tech University of Korea, Siheung 15073, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungtaek","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Tech University of Korea, Siheung 15073, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1093\/ageing\/afab155","article-title":"Why is living alone in older age related to increased mortality risk? A longitudinal cohort study","volume":"50","author":"Abell","year":"2021","journal-title":"Age Ageing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4156","DOI":"10.3390\/ijerph20054156","article-title":"Association between sarcopenia, falls, and cognitive impairment in older people: A systematic review with meta-analysis","volume":"20","author":"Fhon","year":"2023","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"251","DOI":"10.3988\/jcn.2022.0219","article-title":"Relationships of fall risk with frailty, sarcopenia, and balance disturbances in mild-to-moderate Alzheimer\u2019s disease","volume":"19","author":"Oytun","year":"2023","journal-title":"J. Clin. Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1007\/s41999-022-00661-1","article-title":"Association between sarcopenia and cognitive impairment in the older people: A meta-analysis","volume":"13","author":"Chen","year":"2022","journal-title":"Eur. Geriatr. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"43","DOI":"10.35133\/kssche.20180831.04","article-title":"The effects of stress and depression on the quality of life in single elderly households","volume":"19","author":"Kim","year":"2018","journal-title":"J. Korean Soc. Sch. Community Health Educ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1093\/ageing\/afl084","article-title":"Falls in older people: Epidemiology, risk factors and strategies for prevention","volume":"35","author":"Rubenstein","year":"2006","journal-title":"Age Ageing"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, X., Ellul, J., and Azzopardi, G. (2020). Elderly fall detection systems: A literature survey. Front. Robot. AI, 7.","DOI":"10.3389\/frobt.2020.00071"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"18398","DOI":"10.1109\/JSEN.2021.3090454","article-title":"Performance, challenges, and limitations in multimodal fall detection systems","volume":"21","author":"Xefteris","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"166","DOI":"10.3390\/technologies12090166","article-title":"Wearable fall detectors based on low power transmission systems","volume":"12","author":"Villa","year":"2024","journal-title":"Technologies"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.3390\/s17122864","article-title":"Home camera-based fall detection system for the elderly","volume":"17","author":"Brunete","year":"2017","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"77702","DOI":"10.1109\/ACCESS.2019.2922708","article-title":"Research of fall detection and fall prevention technologies: A systematic review","volume":"7","author":"Ren","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","first-page":"6931789","article-title":"Challenges and issues in multisensor fusion approach for fall detection","volume":"2016","author":"Koshmak","year":"2016","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.1109\/JSEN.2017.2697077","article-title":"Radar and RGB-depth sensors for fall detection: A review","volume":"17","author":"Cippitelli","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Z\u00fcrich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Baird, Z., Rajan, S., and Bolic, M. (2018, January 18\u201321). Classification of human posture from radar returns using ultra-wideband radar. Proceedings of the 2018 IEEE Radar Conference (RadarConf18), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8513094"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"13672","DOI":"10.1109\/JSEN.2020.3006386","article-title":"Continuous human activity classification from FMCW radar with Bi-LSTM networks","volume":"20","author":"Shrestha","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liang, T., and Xu, H.A. (2021, January 10\u201312). Posture recognition based fall detection system using a 24GHz CMOS FMCW radar SoC. Proceedings of the IEEE International Conference on Consumer Electronics, Las Vegas, NV, USA.","DOI":"10.1109\/IWS52775.2021.9499555"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7208","DOI":"10.3390\/s23167208","article-title":"Machine learning-based human posture identification from point cloud data acquisitioned by FMCW millimetre-wave radar","volume":"23","author":"Zhang","year":"2023","journal-title":"Sensors"},{"key":"ref_19","unstructured":"Werghi, N., and Xiao, Y. (2002, January 21). Recognition of human body posture from a cloud of 3D data points using wavelet transform coefficients. Proceedings of the IEEE International Conference on Automatic Face Gesture Recognition, Washington, DC, USA."},{"key":"ref_20","unstructured":"Meng, Z., Li, M., Li, X., Yan, H., Liu, Y., and Zhao, Y. (2020, January 7\u201312). Gait recognition for co-existing multiple people using millimeter wave sensing. Proceedings of the AAAI Conference on Artificial Intelligence, Hilton, NY, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5365","DOI":"10.3390\/s24165365","article-title":"Doppler radar sensor-based fall detection using a convolutional bidirectional LSTM model","volume":"24","author":"Zhang","year":"2024","journal-title":"Sensors"},{"key":"ref_22","unstructured":"Liu, Y., Wang, C., Zhao, J., and Li, X. (2023). Effective fall detection and post-fall breath rate tracking using a low-cost CW Doppler radar sensor. Comput. Biol. Med., 163."},{"key":"ref_23","first-page":"17","article-title":"Radar-based elderly fall detection using smoothed pseudo Wigner-Ville distribution and XGBoost learning","volume":"10","author":"Wu","year":"2023","journal-title":"Healthc. Technol. Lett."},{"key":"ref_24","unstructured":"Gong, H., Liu, J., Xu, J., and Wang, Z. (2023). Advanced millimeter-wave radar system for real-time multiple-human tracking and fall detection. arXiv."},{"key":"ref_25","unstructured":"Xie, Z., Zhang, Y., Liu, Y., and Lu, S. (2020). mmFall: Fall detection using 4D mmWave radar and a hybrid variational RNN autoencoder. arXiv."},{"key":"ref_26","first-page":"1221","article-title":"Trends in intelligent radar technology","volume":"36","author":"Koo","year":"2021","journal-title":"Electron. Telecommun. Trends"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sammut, C., and Webb, G.I. (2011). K-Means Clustering. Encyclopedia of Machine Learning, Springer.","DOI":"10.1007\/978-0-387-30164-8"},{"key":"ref_28","first-page":"1","article-title":"MARS: mmWave-based assistive rehabilitation system for smart healthcare","volume":"1","author":"An","year":"2021","journal-title":"ACM Trans. Embed. Comput. Syst. (TECS)"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhang, Y., Wu, Z., and Fu, X. (2024). Enhancing understanding of healthy aging based on time-varying dependencies among multidimensional health, life satisfaction, and health behaviors of older adults aged 60 years and over. BMC Public Health, 24.","DOI":"10.1186\/s12889-024-17752-2"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/11\/3452\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:43:56Z","timestamp":1760031836000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/11\/3452"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,30]]},"references-count":29,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["s25113452"],"URL":"https:\/\/doi.org\/10.3390\/s25113452","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,30]]}}}