Neural network-based estimation of lower limb joint kinematics: A minimally intrusive approach for gait analysis

The establishment of a quantitative gait analysis system holds paramount importance, particularly in the context of functional rehabilitation of the lower limbs. Clinicians emphasize the imperative for sensors to be portable, compact, integrated, and non-intrusive, crucial characteristics in the rehabilitation field to facilitate their use and ensure optimal integration into care protocols. This study investigates an innovative approach aimed at reducing the reliance on body-fixed sensors by harnessing their data within a neural network, thus concentrating on the joint kinematics of the lower limbs. The primary objective is to estimate the flexion-extension angles of the hip, knee, and ankle during walking, utilizing data collected by two sensors positioned on the subject's legs. Initially, the neural network undergoes training with calculated data (leg tilt angles and angular velocities) sourced from the OpenSim database, followed by further refinement with experimental data obtained from a subject walking on a treadmill, wherein leg tilt angles and angular velocities are measured. The significance of this research is underscored by the demonstrated capability, through conducted tests, of the implemented networks to efficiently fuse data from a minimal set of sensors. Consequently, the proposed approach emerges as both practical and minimally intrusive, facilitating a robust evaluation of gait kinematic parameters. •Introducing a novel method that utilizes Artificial Neural Networks to estimate lower limb joint angles during walking, this approach employs just two sensors fixed on the shins. By offering a minimally intrusive solution, it ensures portability and non-intrusiveness crucial for rehabilitation settings, thereby enhancing patient compliance.•Validates the approach using kinematic gait data from OpenSim, affirming its accuracy and efficacy under controlled conditions, laying groundwork for practical application.•Extends methodology to IMU data during treadmill walking, demonstrating adaptability to real-world scenarios and clinical integration.