Abstract
Detecting falls presents a significant challenge for researchers, given the risk of serious injuries like femoral neck fractures, brain hemorrhages, or burns, which result in significant pain and, in some cases, worsen over time, leading to end-of-life complications or even fatalities. One approach to addressing this challenge involves promptly alerting caregivers, such as nurses, upon detecting a fall. In our work, we present a technique to detect falls within a 40-square-meter apartment by collecting data from three ultra-wideband radars. The presented technique combines a vision transformer and a residual neural network for fall identification, a binary classification task distinguishing between fall and non-fall events. To train and test the presented technique, we use data reflecting various fall types simulated by 10 participants across three locations in the apartment. We evaluated the performance of the presented technique in comparison with some base models by using the leave-one-subject-out strategy to demonstrate the generalization of experiment results in practical scenarios with new subjects. We also report our results by applying cross-validation to select a validation set, which highlights the effectiveness of the presented technique during the training phase and demonstrates the confidence of the obtained results in the testing phase. Consistently, the results illustrate the superior performance of the presented technique compared to the based models. Encouragingly, our results indicate nearly 99% accuracy in fall detection, demonstrating promising potential for real-world application.
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This study introduces a fall detection system that solely depends on the three UWB radars positioned within an apartment located in the Laboratoire d’Intelligence Ambiante pour la Reconnaissance d’Activites (LIARA), University of Quebec at Chicoutimi. All the data can be accessed publicly at http://www.kevin-bouchard.ca, Accessed on: 11.03.2024. No datasets were generated during the current study.
References
Shen M, Tsui KL, Nussbaum MA, Kim S, Lure F (2023) An indoor fall monitoring system: Robust, multistatic radar sensing and explainable, feature-resonated deep neural network. IEEE J Biomed Health Inf 27(4):1891–1902
Hung WP, Chang CH (2024) Dual-mode embedded impulse-radio ultra-wideband radar system for biomedical applications. Sens 24(17):5555
Matta L, Sharma B, Sharma M (2024) A review on bandwidth enhancement techniques and band-notched characteristics of mimo-ultra wide band antennas. Wirel Netw 30(3):1339–1382
He J, Zhu W, Qiu L, Zhang Q, Wang C (2024) An indoor fall detection system based on wifi signals and genetic algorithm optimized random forest. Wirel Netw 30(3):1753–1771
Ullmann I, Guendel RG, Kruse NC, Fioranelli F, Yarovoy A (2023) A survey on radar-based continuous human activity recognition. IEEE J Microwaves
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929
Wu H, Xiao B, Codella N, Liu M, Dai X, Yuan L, Zhang L (2021) Cvt: Introducing convolutions to vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 22–31
Zhou D, Kang B, Jin X, Yang L, Lian X, Jiang Z, Hou Q, Feng J (2021) Deepvit: Towards deeper vision transformer. arXiv preprint arXiv:2103.11886 (2021)
Ali A, Touvron H, Caron M, Bojanowski P, Douze M, Joulin A, Laptev I, Neverova N, Synnaeve G, Verbeek J et al (2021) Xcit: Cross-covariance image transformers. Adv Neural Inf Process Syst 34:20014–20027
Renggli C, Pinto AS, Houlsby N, Mustafa B, Puigcerver J, Riquelme C (2022) Learning to merge tokens in vision transformers. arXiv preprint arXiv:2202.12015
Liu Y, Matsoukas C, Strand F, Azizpour H, Smith K (2023) Patchdropout: Economizing vision transformers using patch dropout. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp 3953–3962
Lee SH, Lee S, Song BC (2021) Vision transformer for small-size datasets. arXiv preprint arXiv:2112.13492
Touvron H, Cord M, El-Nouby A, Verbeek J, Jégou H (2022) Three things everyone should know about vision transformers. In: European Conference on Computer Vision, pp 497–515. Springer
Sandler M, Zhmoginov A, Vladymyrov M, Jackson A (2022) Fine-tuning image transformers using learnable memory. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12155–12164
Majib MS, Rahman MM, Sazzad TS, Khan NI, Dey SK (2021) Vgg-scnet: A vgg net-based deep learning framework for brain tumor detection on mri images. IEEE Access 9:116942–116952
Kiliç Ş, Askerzade I, Kaya Y (2020) Using resnet transfer deep learning methods in person identification according to physical actions. IEEE Access 8:220364–220373
Azhagiri M, Rajesh P (2024) Ean: enhanced alexnet deep learning model to detect brain tumor using magnetic resonance images. Multimed Tools Appl 1–17
Bird JJ, Lotfi A (2024) Cifake: Image classification and explainable identification of ai-generated synthetic images. IEEE Access
Gharghan SK, Hashim HA (2024) A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques. Meas 114186
Hu S, Cao S, Toosizadeh N, Barton J, Hector M.G, Fain MJ (2024) Radar-based fall detection: A survey. IEEE Robot Autom Mag
Clemente J, Li F, Valero M, Song W (2019) Smart seismic sensing for indoor fall detection, location, and notification. IEEE J Biomed Health Inf 24(2):524–532
Chen D, Wong AB, Wu K (2023) Fall detection based on fusion of passive and active acoustic sensing. IEEE Int Things J
He C, Liu S, Zhong G, Wu H, Cheng L, Lin J, Huang Q (2023) A non-contact fall detection method for bathroom application based on mems infrared sensors. Micromach 14(1):130
Le Kernec J, Fioranelli F, Ding C, Zhao H, Sun L, Hong H, Lorandel J, Romain O (2019) Radar signal processing for sensing in assisted living: The challenges associated with real-time implementation of emerging algorithms. IEEE Signal Process Mag 36(4):29–41
Hong H, Zhang L, Gu C, Li Y, Zhou G, Zhu X (2018) Noncontact sleep stage estimation using a cw doppler radar. IEEE J Emerg Select Top Circ Syst 8(2):260–270
Ma L, Li X, Liu G, Cai Y (2023) Fall direction detection in motion state based on the fmcw radar. Sens 23(11):5031
Maitre J, Bouchard K, Gaboury S (2020) Fall detection with uwb radars and cnn-lstm architecture. IEEE J Biomed Health Inf 25(4):1273–1283
Imbeault-Nepton T, Maître J, Bouchard K, Gaboury S (2023) Fall detection from uwb radars: A comparative analysis of deep learning and classical machine learning techniques. In: Proceedings of the 2023 ACM Conference on Information Technology for Social Good, pp 197–204
Erol B, Amin MG, Boashash B (2017) Range-doppler radar sensor fusion for fall detection. In: 2017 IEEE Radar Conference (RadarConf), pp 0819–0824. IEEE
Seyfioğlu MS, Özbayoğlu AM, Gürbüz SZ (2018) Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities. IEEE Trans Aerosp Electr Syst 54(4):1709–1723
Shrestha A, Le Kernec J, Fioranelli F, Cippitelli E, Gambi E, Spinsante S (2017) Feature diversity for fall detection and human indoor activities classification using radar systems. In: International Conference on Radar Systems (Radar 2017), IET
Liang T, Liu R, Yang L, Lin Y, Shi CJR, Xu H (2024) Fall detection system based on point cloud enhancement model for 24 ghz fmcw radar. Sens 24(2):648
Sadreazami H, Bolic M, Rajan S (2019) Fall detection using standoff radar-based sensing and deep convolutional neural network. IEEE Trans Circ Syst II Express Briefs 67(1):197–201
Wang P, Li Q, Yin P, Wang Z, Ling Y, Gravina R, Li Y (2023) A convolution neural network approach for fall detection based on adaptive channel selection of uwb radar signals. Neural Comput Appl 35(22):15967–15980
Wang Y, Zhou J, Tong J, Wu X (2019) Uwb-radar-based synchronous motion recognition using time-varying range-doppler images. IET Radar Sonar Navig 13(12):2131–2139
Li H, Shrestha A, Heidari H, Le Kernec J, Fioranelli F (2020) Bi-lstm network for multimodal continuous human activity recognition and fall detection. IEEE Sens J 20(3):1191–1201
Baik JY, Shin HC (2024) Fall detection using fmcw radar to reduce detection errors for the elderly. Journal of Electromagnetic Eng Sci 24(1):78–88
Arnaoutoglou DG, Dedemadis D, Kyriakou AA, Katsimentes S, Grekidis A, Menychtas D, Aggelousis N, Sirakoulis GC, Kyriacou GA (2024) Acceleration-based low-cost cw radar system for real-time elderly fall detection. IEEE J Electromagn RF Microwaves Med Biol
Sadreazami H, Bolic M, Rajan S (2021) Contactless fall detection using time-frequency analysis and convolutional neural networks. IEEE Trans Ind Inf 17(10):6842–6851
Lu J, Ye WB (2022) Design of a multistage radar-based human fall detection system. IEEE Sens J 22(13):13177–13187
Yang L, Ye W (2024) Design of a two-stage continuous fall detection system using multiframe radar range-doppler maps. IEEE Sens J
Pardhu T, Kumar V, Kanavos A, Gerogiannis VC, Acharya B (2024) Enhanced classification of human fall and sit motions using ultra-wideband radar and hidden markov models. Math 12(15):2314
Stankovic L, Dakovic M, Thayaparan T (2014) Time-frequency Signal Analysis with Applications. Artech house, ???
Erol B, Francisco M, Ravisankar A, Amin M (2018) Realization of radar-based fall detection using spectrograms. In: Compressive Sensing VII: From Diverse Modalities to Big Data Analytics, vol 10658, pp 77–88. SPIE
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 10012–10022
Acknowledgements
The authors wish to acknowledge King Fahd University of Petroleum & Minerals (KFUPM) and SDAIA-KFUPM Joint Research Center for Artificial Intelligence (JRC-AI) for providing the facilities to carry out this research.
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Abudalfa, S., Bouchard, K. A hybrid vision transformer and residual neural network model for fall detection using UWB radars. Appl Intell 55, 222 (2025). https://doi.org/10.1007/s10489-024-06156-9
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DOI: https://doi.org/10.1007/s10489-024-06156-9