Abstract
Soft sensors combine a hardware component with an intelligent algorithmic processing of the raw sensor signals. While individualization of software components according to a person’s specific needs is comparably cheap, individualization of the sensor hardware itself is usually impossible in mass production. At the same time, the number of raw sensors should be minimum to reduce production costs. In this contribution, we propose to model this challenge as a feature selection problem, which optimizes a feature set simultaneously with respect to a family of functions corresponding to individualized post-processing of sensor signals. This concept is integrated into a number of different classical feature selection schemes, and evaluated in the context of the placement of pressure sensors as part of a shoe insole. It turns out that feature selection respecting the class of functions is superior to both placement based on anatomical considerations and classical feature selection methods.
The project has been funded by the Ministry of Culture and Science of the Federal State North Rhine-Westphalia in the frame of the project RoSe in the AI-graduate-school https://dataninja.nrw/.
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References
Blickhan, R., Seyfarth, A., Geyer, H., Grimmer, S., Wagner, H., Günther, M.: Intelligence by mechanics. Phil. Trans. R. Soc. Math. Phys. Eng. Sci. 365(1850), 199–220 (2007). https://doi.org/10.1098/rsta.2006.1911
Bradski, G.: The OpenCV library. J. Softw. Tools 25, 120–123 (2000)
Dhal, P., Azad, C.: A comprehensive survey on feature selection in the various fields of machine learning. Appl. Intell. 52(4), 4543–4581 (2022). https://doi.org/10.1007/s10489-021-02550-9
Ha, S., Park, S., Lim, H., Baek, S.H., Kim, D.K., Yoon, S.H.: The placement position optimization of a biosensor array for wearable healthcare systems. J. Mech. Sci. Technol. 33(7), 3237–3244 (2019). https://doi.org/10.1007/s12206-019-0619-0
Hughes, A.J.: Statistics: A Foundation for Analysis. Addison-Wesley Pub. Co., Reading (1971). http://archive.org/details/trent_0116302260611
Leite, M., Soares, B., Lopes, V., Santos, S., Silva, M.T.: Design for personalized medicine in orthotics and prosthetics. Procedia CIRP 84, 457–461 (2019). https://www.sciencedirect.com/science/article/pii/S2212827119309011. https://doi.org/10.1016/j.procir.2019.04.254
Losing, V., Hammer, B., Wersing, H.: Incremental on-line learning: a review and comparison of state of the art algorithms. Neurocomputing 275, 1261–1274 (2018). https://www.sciencedirect.com/science/article/pii/S0925231217315928. https://doi.org/10.1016/j.neucom.2017.06.084
Ometov, A., et al.: A survey on wearable technology: history, state-of-the-art and current challenges. Comput. Netw. 193, 108074 (2021). https://www.sciencedirect.com/science/article/pii/S1389128621001651. https://doi.org/10.1016/j.comnet.2021.108074
Oostenveld, R., Praamstra, P.: The five percent electrode system for high-resolution EEG and ERP measurements. Clin. Neurophysiol. 112(4), 713–719 (2001). https://www.sciencedirect.com/science/article/pii/S1388245700005277. https://doi.org/10.1016/S1388-2457(00)00527-7
Pati, Y., Rezaiifar, R., Krishnaprasad, P.: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 40–44 (1993). https://doi.org/10.1109/ACSSC.1993.342465. ISSN: 1058–6393
Pedregosa, F., et al.: SciKit-learn: machine learning in python. J. Mach. Learn. Res. 12(85), 2825–2830 (2011). http://jmlr.org/papers/v12/pedregosa11a.html
Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997). https://doi.org/10.1023/A:1008202821328
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.) 58(1), 267–288 (1996). https://onlinelibrary.wiley.com/doi/10.1111/j.2517-6161.1996.tb02080.x. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
ECG Lead Placement - Normal Function of the Heart - Cardiology Teaching Package - Practice Learning - Division of Nursing - The University of Nottingham. https://www.nottingham.ac.uk/nursing/practice/resources/cardiology/function/placement_of_leads.php
Virtanen, P., et al.: SciPy 1.0: fundamental algorithms for scientific computing in python. Nat. Methods 17(3), 261–272 (2020). https://www.nature.com/articles/s41592-019-0686-2. https://doi.org/10.1038/s41592-019-0686-2
Xiang, Y., Gubian, S., Suomela, B., Hoeng, J.: Generalized simulated annealing for global optimization: the GenSA package. R J. 5(1), 13 (2013). https://journal.r-project.org/archive/2013/RJ-2013-002/index.html. https://doi.org/10.32614/RJ-2013-002
Yoo, S., Gil, H., Kim, J., Ryu, J., Yoon, S., Park, S.K.: The optimization of the number and positions of foot pressure sensors to develop smart shoes. J. Ergon. Soc. Korea 36, 15 (2017). https://doi.org/10.5143/JESK.2017.36.5.395
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Vieth, M., Grimmelsmann, N., Schneider, A., Hammer, B. (2022). Efficient Sensor Selection for Individualized Prediction Based on Biosignals. In: Yin, H., Camacho, D., Tino, P. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2022. IDEAL 2022. Lecture Notes in Computer Science, vol 13756. Springer, Cham. https://doi.org/10.1007/978-3-031-21753-1_32
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