An IMUs-Based Extended Kalman Filter to Estimate Gait Lower Limb Sagittal Kinematics for the Control of Wearable Robotic Devices

Inertial sensors have gained relevance as wearable sensors to acquire the kinematics of human limbs through fusion sensor algorithms and biomechanical models. However, there are some limitations to the use of Inertial Measurement Units in the control of wearable robotic devices: 1) Some approaches use magnetometer readings to estimate the orientation of the sensor, and, as a result, they are prone to errors due to electromagnetic interferences; 2) Biomechanical model-based approaches require complex and time-consuming calibration procedures. In order to address these issues, this paper proposes an Extended Kalman Filter to estimate sagittal lower limb kinematics during gait, based on gyroscopes and accelerometers and without requiring any calibration or sensor alignment process. As magnetometer measurements are not involved, this method is not affected by electromagnetic disturbances. Our approach calculates the knee rotation axis in real-time, and it estimates hip and ankle sagittal axes considering that the movements in that plane occur around parallel axes. We carried out an experimental validation with eight healthy subjects walking on a treadmill at different velocities. We obtained waveform RMS errors about 3.8°, 3.6°, and 4.8° for hip, knee, and ankle in the sagittal plane. We also assessed the performance of this method as a tool for controlling lower-limb robotic exoskeletons by detecting gait events or estimating the phase and frequency of the gait in real-time through an Adaptive Frequency Oscillator. The average RMS delay in the detection of gait events was lower than 60 ms, and the RMSE in the estimation of the gait phase was about 3% of the gait cycle. We conclude that the described method could be used as a controller for wearable robotic devices.