Abstract
Tiny Machine Learning (TinyML) allows to move the intelligence processing as close as possible to where data are generated, hence reducing the latency with which a decision is made and being able to process data even when remote connection is scarce or absent. In this technological scenario, Ultra-Wideband (UWB) radar data represent a new and challenging source of data providing relevant information, while guaranteeing the privacy of users. This paper introduces a novel TinyML solution able to count the number of people in a given area by processing UWB radar data. This novel solution was carefully designed to guarantee a high counting accuracy, while reducing the memory and computational demand so as to be executed on tiny devices. Experimental results on a real-world UWB radar dataset show the effectiveness of the proposed solution.
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References
Codecarbon. https://codecarbon.io/. Accessed 30 Sept 2010
Alippi, C., Disabato, S., Roveri, M.: Moving convolutional neural networks to embedded systems: the AlexNet and VGG-16 case. In: 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), pp. 212–223 (2018). https://doi.org/10.1109/IPSN.2018.00049
Alippi, C., Disabato, S., Roveri, M.: Moving convolutional neural networks to embedded systems: the AlexNet and VGG-16 case. In: 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), pp. 212–223. IEEE (2018)
Alippi, C., Fantacci, R., Marabissi, D., Roveri, M.: A cloud to the ground: the new frontier of intelligent and autonomous networks of things. IEEE Commun. Mag. 54(12), 14–20 (2016). https://doi.org/10.1109/MCOM.2016.1600541CM
Alippi, C., Roveri, M.: The (not) far-away path to smart cyber-physical systems: an information-centric framework. Computer 50(4), 38–47 (2017). https://doi.org/10.1109/MC.2017.111
Choi, J.W., Yim, D.H., Cho, S.H.: People counting based on an IR-UWB radar sensor. IEEE Sens. J. 17(17), 5717–5727 (2017). https://doi.org/10.1109/JSEN.2017.2723766
Chowdhery, A., Warden, P., Shlens, J., Howard, A., Rhodes, R.: Visual Wake Words Dataset. arXiv:1906.05721 [cs, eess] (2019). http://arxiv.org/abs/1906.05721, arXiv: 1906.05721
David, R., Duke, J., et al.: TensorFlow lite micro: embedded machine learning for TinyML systems. Proc. Mach. Learn. Syst. 3, 800–811 (2021)
Disabato, S., Roveri, M.: Reducing the computation load of convolutional neural networks through gate classification. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2018)
Ha, T., Kim, J.: Detection and localization of multiple human targets based on respiration measured by IR-UWB radars. In: 2019 IEEE SENSORS, pp. 1–4 (2019). https://doi.org/10.1109/SENSORS43011.2019.8956687. ISSN 2168-9229
Jacob, B., Kligys, S., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2704–2713 (2018)
Kim, Y., Moon, T.: Human detection and activity classification based on micro-doppler signatures using deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 13(1), 8–12 (2016). https://doi.org/10.1109/LGRS.2015.2491329
Koks, D.: How to create and manipulate radar range–doppler plots, p. 95 (2014)
Lang, Y., Hou, C., Yang, Y., Huang, D., He, Y.: Convolutional neural network for human micro-doppler classification. In: European Microwave Conference (2017)
Liang, F., et al.: Detection of multiple stationary humans using UWB MIMO radar. Sensors 16, 1922 (2016). https://doi.org/10.3390/s16111922
Liu, J., Tripathi, S., Kurup, U., Shah, M.: Pruning algorithms to accelerate convolutional neural networks for edge applications: a survey. arXiv preprint arXiv:2005.04275 (2020)
Louis Moreau, M.K.: Announcing FOMO (faster objects, more objects) (2022). https://www.edgeimpulse.com/blog/announcing-fomo-faster-objects-more-objects
Park, J., Javier, R.J., Moon, T., Kim, Y.: Micro-doppler based classification of human aquatic activities via transfer learning of convolutional neural networks. Sensors 16(12), 1990 (2016). https://doi.org/10.3390/s16121990. https://www.mdpi.com/1424-8220/16/12/1990
Pavan, M., Caltabiano, A., Roveri, M.: On-device subject recognition in UWB-radar data with tiny machine learning. In: CEUR Workshop Proceedings (2022)
Pavan, M., Clatabiano, A., Roveri, M.: TinyML for UWB-radar based presence detection. In: Proceedings of WCCI 2022, p. 5. IEEE, July 2022
Pham, C.T., Luong, V.S., Nguyen, D.K., Vu, H.H.T., Le, M.: Convolutional neural network for people counting using UWB impulse radar. J. Instrum. 16(08), P08031 (2021). https://doi.org/10.1088/1748-0221/16/08/P08031. https://dx.doi.org/10.1088/1748-0221/16/08/P08031
Prakash, S., et al.: Is TinyML sustainable? Assessing the environmental impacts of machine learning on microcontrollers (2023)
Ray, P.P.: A review on TinyML: state-of-the-art and prospects 34(4), 1595–1623 (2022). https://doi.org/10.1016/j.jksuci.2021.11.019. https://www.sciencedirect.com/science/article/pii/S1319157821003335
Sanchez-Iborra, R., Skarmeta, A.F.: TinyML-enabled frugal smart objects: challenges and opportunities. IEEE Circuits Syst. Mag. 20(3), 4–18 (2020)
Shao, Y., Guo, S., Sun, L., Chen, W.: Human motion classification based on range information with deep convolutional neural network. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 1519–1523 (2017). https://doi.org/10.1109/ICISCE.2017.317
Warden, P.: Speech commands: a dataset for limited-vocabulary speech recognition. arXiv:1804.03209 [cs] (2018). http://arxiv.org/abs/1804.03209, arXiv: 1804.03209
Warden, P., Situnayake, D.: TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-power Microcontrollers. O’Reilly (2020). Google-Books-ID: sB3mxQEACAAJ
Weiß, J., Pérez, R., Biebl, E.: Improved people counting algorithm for indoor environments using 60 GHz FMCW radar. In: 2020 IEEE Radar Conference (RadarConf 2020), pp. 1–6 (2020). https://doi.org/10.1109/RadarConf2043947.2020.9266607. ISSN 2375-5318
Yang, X., Yin, W., Zhang, L.: People counting based on CNN using IR-UWB radar. In: 2017 IEEE/CIC International Conference on Communications in China (ICCC), pp. 1–5 (2017). https://doi.org/10.1109/ICCChina.2017.8330453
Zach: Z-score normalization: definition & examples (2021). https://www.statology.org/z-score-normalization/
Acknowledgments
The authors would like to thank Gabriele Viscardi, Pierpaolo Lento and Alessandro Basso for the valuable support in the development of this work. This paper is supported by PNRR-PE-AI FAIR project funded by the NextGeneration EU program.
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Pavan, M., Navarro, L.G., Caltabiano, A., Roveri, M. (2025). Unveiling the Potential of Tiny Machine Learning for Enhanced People Counting in UWB Radar Data. In: Meo, R., Silvestri, F. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2023. Communications in Computer and Information Science, vol 2136. Springer, Cham. https://doi.org/10.1007/978-3-031-74640-6_13
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