{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:29:51Z","timestamp":1785407391237,"version":"3.56.0"},"reference-count":34,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"crossref","award":["RS-2022-00144290"],"award-info":[{"award-number":["RS-2022-00144290"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"crossref","award":["RS-2024-00433615"],"award-info":[{"award-number":["RS-2024-00433615"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"crossref"}]},{"name":"IDEC"},{"id":[{"id":"https:\/\/ror.org\/04zzddp49","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As the number of elderly individuals living alone increases, the risk of fall-related accidents correspondingly rises, underscoring the need for rapid fall detection systems. Because falls are difficult to predict in terms of location, detection systems must be deployed in a distributed manner, which in turn requires compact and low-power implementations. Unlike camera sensors, radar sensors do not raise privacy concerns and are not limited by line-of-sight constraints. Moreover, compared with wearable sensors, radar enables continuous monitoring without user intervention. However, prior radar-based approaches incur high computational complexity, leading to increased power consumption and larger hardware area, thereby necessitating efficient hardware design. This paper proposes a lightweight fall detection system based on continuous-wave (CW) radar and a binarized convolutional neural network (BCNN). Radar signals are preprocessed using short-time Fourier transform (STFT) to generate binary spectrograms, which are then fed into a BCNN-based classification network. The proposed system performs binary classification of five fall activities and seven non-fall activities with an accuracy of 96.1%. The preprocessing module and classification network were implemented as hardware accelerators and integrated with a microprocessor in a system-on-chip (SoC) architecture on a field-programmable gate array (FPGA). Compared with the software implementation, the proposed hardware achieved speedups of 387.5\u00d7 and 86.7\u00d7 for the preprocessing and classification modules, respectively. Furthermore, the overall system processing time was 2.58 ms, corresponding to an 89.5\u00d7 speedup over the software baseline.<\/jats:p>","DOI":"10.3390\/s26082469","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T10:11:04Z","timestamp":1776420664000},"page":"2469","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FPGA Implementation of a Radar-Based Fall Detection System Using Binarized Convolutional Neural Networks"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1477-0991","authenticated-orcid":false,"given":"Hyeongwon","family":"Cho","sequence":"first","affiliation":[{"name":"Department of Smart Air Mobility, Korea Aerospace University, Goyang 10540, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0015-1768","authenticated-orcid":false,"given":"Soongyu","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Smart Air Mobility, Korea Aerospace University, Goyang 10540, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2299-9911","authenticated-orcid":false,"given":"Yunho","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Smart Air Mobility, Korea Aerospace University, Goyang 10540, Republic of Korea"},{"name":"School of Electronics and Information Engineering, Korea Aerospace University, Goyang 10540, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"ref_1","first-page":"142","article-title":"Elderly Fall Detection Using Wearable Sensors: A Low Cost Highly Accurate Algorithm","volume":"10","author":"Saleh","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15219","DOI":"10.1109\/JSEN.2024.3375603","article-title":"A Novel Embedded Deep Learning Wearable Sensor for Fall Detection","volume":"24","author":"Campanella","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/JSEN.2025.3602945","article-title":"FIBiLS: Fall Detection of Healthy Elderly Using IMU Sensor and BiLSTM Model","volume":"25","author":"Gaud","year":"2025","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8293","DOI":"10.1109\/JSEN.2019.2918690","article-title":"Development of an Enhanced Threshold-Based Fall Detection System Using Smartphones with Built-In Accelerometers","volume":"19","author":"Lee","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1109\/TNSRE.2019.2911602","article-title":"A Patient-Specific Single Sensor IoT-Based Wearable Fall Prediction and Detection System","volume":"27","author":"Saadeh","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"28304","DOI":"10.1109\/JSEN.2023.3314828","article-title":"Fall Detection With a Nonintrusive and First-Person Vision Approach","volume":"23","author":"Wang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"14812","DOI":"10.1109\/JSEN.2024.3379167","article-title":"SKIP: Accurate Fall Detection Based on Skeleton Keypoint Association and Critical Feature Perception","volume":"24","author":"Du","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14460","DOI":"10.1109\/JSEN.2023.3276891","article-title":"Future Frame Prediction Network for Human Fall Detection in Surveillance Videos","volume":"23","author":"Li","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"114966","DOI":"10.1109\/ACCESS.2019.2936320","article-title":"An integrated vision-based approach for efficient human fall detection in a home environment","volume":"7","author":"Harrou","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1806","DOI":"10.1109\/TSMC.2018.2850149","article-title":"Deep convolutional neural networks for human action recognition using depth maps and postures","volume":"49","author":"Kamel","year":"2018","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"166173","DOI":"10.1109\/ACCESS.2019.2947739","article-title":"A machine learning approach for fall detection based on the instantaneous Doppler frequency","volume":"7","author":"Chelli","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"16969","DOI":"10.1109\/JSEN.2021.3079835","article-title":"Elderly fall detection with vital signs monitoring using CW Doppler radar","volume":"21","author":"Hanifi","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Erol, B., and Amin, M.G. (2018, January 15\u201320). Radar data cube analysis for fall detection. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8461512"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ding, C., Zou, Y., Sun, L., Hong, H., Zhu, X., and Li, C. (2019, January 19\u201322). Fall detection with multi-domain features by a portable FMCW radar. Proceedings of the 2019 IEEE MTT-S International Wireless Symposium (IWS), Guangzhou, China.","DOI":"10.1109\/IEEE-IWS.2019.8804036"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"13364","DOI":"10.1109\/JSEN.2020.3006918","article-title":"A millimetre-wave radar-based fall detection method using line kernel convolutional neural network","volume":"20","author":"Wang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7968","DOI":"10.1109\/JSEN.2023.3245063","article-title":"Unobtrusive human fall detection system using mmwave radar and data driven methods","volume":"23","author":"Rezaei","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1109\/TCSII.2019.2904498","article-title":"Fall detection using standoff radar-based sensing and deep convolutional neural network","volume":"67","author":"Sadreazami","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Express Briefs"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yoshino, H., Moshnyaga, V.G., and Hashimoto, K. (2019, January 6\u20139). Fall Detection on a single Doppler Radar Sensor by using Convolutional Neural Networks. Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy.","DOI":"10.1109\/SMC.2019.8914303"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Anishchenko, L., Zhuravlev, A., and Chizh, M. (2019). Fall detection using multiple bioradars and convolutional neural networks. Sensors, 19.","DOI":"10.3390\/s19245569"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sun, Y., Hang, R., Li, Z., Jin, M., and Xu, K. (2019, January 1\u20134). Privacy-Preserving Fall Detection with Deep Learning on mmWave Radar Signal. Proceedings of the 2019 IEEE Visual Communications and Image Processing (VCIP), Sydney, NSW, Australia.","DOI":"10.1109\/VCIP47243.2019.8965661"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"175179","DOI":"10.1109\/ACCESS.2025.3605387","article-title":"Real-Time Fall Detection With Hybrid CNN-LSTM Using IWR6843AOP FMCW Radar","volume":"13","author":"Tan","year":"2025","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ma, L., Liu, M., Wang, N., Wang, L., Yang, Y., and Wang, H. (2020). Room-level fall detection based on ultra-wideband (UWB) monostatic radar and convolutional long short-term memory (LSTM). Sensors, 20.","DOI":"10.3390\/s20041105"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1109\/JBHI.2020.3027967","article-title":"Fall detection with UWB radars and CNN-LSTM architecture","volume":"25","author":"Maitre","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cho, H., Kang, S., Sim, Y., Lee, S., and Jung, Y. (2025). Fall detection based on continuous wave radar sensor using binarized neural networks. Appl. Sci., 15.","DOI":"10.3390\/app15020546"},{"key":"ref_25","first-page":"77","article-title":"Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities","volume":"54","author":"Chen","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_26","first-page":"031003","article-title":"Sensors in autonomous vehicles: A survey","volume":"1","author":"Ayala","year":"2021","journal-title":"J. Auton. Veh. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xiao, L., Zhao, L., and Jin, J. (2025). Preset-time convergence fuzzy zeroing neural network for chaotic system synchronization: FPGA validation and secure communication applications. Sensors, 25.","DOI":"10.3390\/s25175394"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1109\/TCAD.2023.3340128","article-title":"A distributed and parallel accelerator design for 3-D acoustic imaging on FPGA-based systems","volume":"43","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kang, S., Kim, H., Park, C., Sim, Y., Lee, S., and Jung, Y. (2023). sEMG-based hand gesture recognition using binarized neural network. Sensors, 23.","DOI":"10.3390\/s23031436"},{"key":"ref_30","unstructured":"Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., and Bengio, Y. (2016). Binarized neural networks: Training deep neural networks with weights and activations constrained to + 1 or \u22121. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1016\/j.neucom.2017.09.046","article-title":"FP-BNN: Binarized neural network on FPGA","volume":"275","author":"Liang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_32","unstructured":"(2026, March 17). ZCU104 Evaluation Kit. Available online: https:\/\/www.amd.com\/ko\/products\/adaptive-socs-and-fpgas\/evaluation-boards\/zcu104.html."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhuang, J., Chen, W., Guo, B., and Yan, Y. (2024). Infrared weak target detection in dual images and dual areas. Remote Sens., 16.","DOI":"10.3390\/rs16193608"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"8253","DOI":"10.1109\/TITS.2023.3264507","article-title":"Automotive radar optimization design in a spectrally crowded V2I communication environment","volume":"24","author":"Yao","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/8\/2469\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T04:13:07Z","timestamp":1776571987000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/8\/2469"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,17]]},"references-count":34,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["s26082469"],"URL":"https:\/\/doi.org\/10.3390\/s26082469","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,17]]}}}