Signal Processing and Machine Learning for Sensor Systems

Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy

Amal Kammoun, Philippe Ravier, Olivier Buttelli

PRISME Laboratory, University of Orleans, Orleans, France


insole measurement, force plate measurement, GRF component estimation, normalization methods, machine learning, PCA pre-normalization


Ground reaction force (GRF) components can be estimated using insole pressure sensors. Principal component analysis in conjunction with machine learning (PCA-ML) methods are widely used for this task. PCA reduces dimensionality and requires pre-normalization. In this paper, we evaluated the impact of twelve pre-normalization methods using three PCA-ML methods on the accuracy of GRF component estimation. Accuracy was assessed using laboratory data from gold-standard force plate measurements. Data were collected from nine subjects during slow- and normal-speed walking activities. We tested the ANN (artificial neural network) and LS (least square) methods while also exploring support vector regression (SVR), a method not previously examined in the literature, to the best of our knowledge. In the context of our work, our results suggest that the same normalization method can produce the worst or the best accuracy results, depending on the ML method. For example, the body weight normalization method yields good results for PCA-ANN but the worst performance for PCA-SVR. For PCA-ANN and PCA-LS, the vector standardization normalization method is recommended. For PCA-SVR, the mean method is recommended. The final message is not to define a normalization method a priori independently of the ML method.

Moticon's Summary

The paper discusses the importance of normalization in machine learning (ML) and principal component analysis (PCA) for estimating ground reaction force (GRF) components using sensor insole data. The study evaluates twelve normalization methods and three ML techniques (artificial neural networks (ANN), least squares (LS), and support vector regression (SVR)) to determine the most effective combinations. The experimental setup involved nine healthy male subjects performing walking tasks, with data collected using Moticon sensor insoles and force plates. Results indicate that the PCA-ANN method with variable standardization (VS) normalization generally provided the best estimation accuracy for GRF components, followed by PCA-SVR and PCA-LS methods. The study highlights the variability in effectiveness of normalization methods depending on the ML technique used, challenging previous assumptions that certain normalization methods, like Z-score, are universally optimal.

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