An artificial neural network framework for lower limb motion signal estimation with foot-mounted inertial sensors

This paper proposes a novel artificial neural network based method for real-time gait analysis with minimal number of Inertial Measurement Units (IMUs). Accurate lower limb attitude estimation has great potential for clinical gait diagnosis for orthopaedic patients and patients with neurological diseases. However, the use of multiple wearable sensors hinder the ubiquitous use of inertial sensors for detailed gait analysis. This paper proposes the use of two IMUs mounted on the shoes to estimate the IMU signals at the shin, thigh and waist for accurate attitude estimation of the lower limbs. By using the artificial neural network framework, the gait parameters, such as angle, velocity and displacements of the IMUs can be estimated. The experimental results have shown that the proposed method can accurately estimate the IMUs signals on the lower limbs based only on the IMU signals on the shoes, which demonstrates its potential for lower limb motion tracking and real-time gait analysis.