Abstract
Commercial pressure insoles use a high number of small sensor elements for the in-field measurement of vertical ground reaction forces (VGRFs) with high spatial resolution. However, the energy demands and costs of these insoles are high. Thus, the use of a smaller sub-set of sensors is proposed by various authors. Thereby, the placement of the sensor elements is chosen based on anatomical landmarks of the foot. In this work, we investigate the optimal placement of a subset of sensor elements for the reconstruction of VGRFs using a data-driven approach based on particle swarm optimization. Results show, that a data-driven placement of the sensor elements reduces the root mean squared error of the reconstructed VGRFs compared to a sensor placement based on anatomical landmarks.
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Acknowledgements
This work was conducted during the Connected Movement research project supported by VDI/VDE-IT. Bjoern Eskofier gratefully acknowledges the support of the German Research Foundation (DFG) within the framework of the Heisenberg professorship programme (grant number ES 434/8-1).
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Zrenner, M., Stoeve, M., Franklin, S., Kumar, B., Jensen, U., Eskofier, B.M. (2022). Data-Driven Optimization of Sensor Placement for Pressure Insoles Using Particle Swarm Optimization. In: Baca, A., Exel, J., Lames, M., James, N., Parmar, N. (eds) Proceedings of the 9th International Performance Analysis Workshop and Conference & 5th IACSS Conference. PACSS 2021. Advances in Intelligent Systems and Computing, vol 1426. Springer, Cham. https://doi.org/10.1007/978-3-030-99333-7_27
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DOI: https://doi.org/10.1007/978-3-030-99333-7_27
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