Centre of pressure estimation during walking using only inertial-measurement units and end-to-end statistical modelling

TL;DR

This paper introduces a novel method to estimate the centre of pressure during walking using only IMU data and statistical models, achieving accuracy comparable to or better than existing methods.

Contribution

It presents a new approach that estimates COP solely from IMU data with minimal calibration and sensor setup, using both linear and LSTM models.

Findings

Achieved average intra-subject RMS error of 12.3mm

Average inter-subject RMS error of 23.7mm

Magnetic data can be omitted without performance loss

Abstract

Estimation of the centre of pressure (COP) is an important part of the gait analysis, for example, when evaluating the functional capacity of individuals affected by motor impairment. Inertial measurement units (IMUs) and force sensors are commonly used to measure gait characteristic of healthy and impaired subjects. We present a methodology for estimating the COP solely from raw gyroscope, accelerometer, and magnetometer data from IMUs using statistical modelling. We demonstrate the viability of the method using an example of two models: a linear model and a non-linear Long-Short-Term Memory (LSTM) neural network model. Models were trained on the COP ground truth data measured using an instrumented treadmill and achieved the average intra-subject root mean square (RMS) error between estimated and ground truth COP of 12.3mm and the average inter-subject RMS error of 23.7mm which is comparable or better than similar studies so far. We show that the calibration procedure in the instrumented treadmill can be as short as a couple of minutes without the decrease in our model performance. We also show that the magnetic component of the recorded IMU signal, which is most sensitive to environmental changes, can be safely dropped without a significant decrease in model performance. Finally, we show that the number of IMUs can be reduced to five without deterioration in the model performance.