  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.
