Work

Selection of insole pressure sensors for ground reaction force estimation through studying principal component analysis

Year: 2025

Type: article

Abstract: • A PCA-based method is proposed to identify the number and location of insole pressure sensors. • Removing the least important sensors improved the estimation of the GRF vertical component. • The higher the number of sensors, the better the estimate, not true. • The intensity of pressure on a sensor does not prejudge its selection. • PCA-selection is preferable than PCA-reduction. In the context of low-cost and portable device for measuring pressure using insole system, selection of the relevant sensors is addressed. In a preliminary step, we compared the accuracy of Ground Reaction Force (GRF) components estimation between two pressure insoles: Fscan and Moticon. This estimation was done using Artificial Neural Network combined with Principal Component Analysis (PCA). Secondly, the focus of this study was to identify the optimal numbers and locations of the pressure sensors by a sensor ranking procedure for both insoles using PCA and three selection strategies. The ranking is determined by analyzing the loss value obtained through PCA for each pressure sensor with three selection strategies. Using data from gold standard force plates, we assessed GRF components estimation accuracies and sensor locations for both insoles during walking activities. As a first result, in our context, Moticon insole yielded superior performance for estimating GRF components compared to Fscan. Secondly, the selection procedure allowed deleting 3 among 16 sensors for Moticon (both feet) and 33/30 (left foot/right foot) among 64 sensors for Fscan. Finally, we have validated these optimal numbers by showing that the quality of GRF components estimation was minimally impacted. Remarkably, both insoles with fewer sensors led to better vertical component estimations. These results should be considered in the context of this study, which does not claim to be generalizable. As these results do not reflect a wide range of activities and subject profiles, it is therefore necessary to re-evaluate these selections with other activity conditions.

Source: Biomedical Signal Processing and Control

DOI: 10.1016/j.bspc.2025.107848

Authors: Amal Kammoun, Philippe Ravier, Olivier Buttelli

Institutions: Université d'Orléans, Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique, Sport, Activité Physique, Rééducation et Motricité pour la performance et la santé

Language: English

Topic: Lower Extremity Biomechanics and Pathologies

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,590