Centre of Pressure Estimation during Walking Using Only Inertial-Measurement Units and End-To-End Statistical Modelling

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 12.3 mm and the average inter-subject RMS error of 23.7 mm 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.

Joint Torque Prediction via Hybrid Neuromusculoskeletal Modelling during Gait Using Statistical Ground Reaction Estimates: An Exploratory Study

“…In comparison, this study obtained the mean RMSEs for AP (1.7% BW) and V (4.5% BW) GRF. Regarding COP, the observed mean RMSE here for AP direction (9.2 mm) seemed to outperform that reported in Podobnik et al [ 57 ] where the corresponding value of 14.9 mm was noted. On the other hand, our average RMSE for ML direction (10.6 mm) was higher than their reported value (9.0 mm).…”

INS Error Estimation Based on an ANFIS and Its Application in Complex and Covert Surroundings

“…The position error of an INS solution, the position error predicted by the ANN model, and the position error predicted by the ANFIS were compared. Furthermore, LSTM, which is an advanced and improved method based on RNN [11,13,50,51], was compared with ANFIS in the position error prediction.…”

Performance Evaluation of IMU and DVL Integration in Marine Navigation

“…Many researchers have studied single-IMU dead-reckoning navigation and optimized algorithms to reduce error accumulation in IMUs [ 4 , 9 , 10 , 11 , 12 , 13 , 14 ]. Low-pass filtering has been used to optimize the calculation results of low-cost shipboard IMUs [ 4 ].…”

“…Nonetheless, under bad weather conditions, the angular error in roll and pitch measurements of low-cost IMUs can be up to 0.09°. Because the continuous and irregular navigation motion on the sea cannot be corrected by the device itself, low-cost IMUs alone are not reliable for navigation dead-reckoning [ 14 ].…”