Background: Gait retraining interventions using real-time biofeedback have been proposed to alter the loading across the knee joint in patients with knee osteoarthritis. Despite the demonstrated benefits of these conservative treatments, their clinical adoption is currently obstructed by the high complexity, spatial demands, and cost of optical motion capture systems. In this study we propose and evaluate a wearable visual feedback system for gait retraining of the foot progression angle (FPA). Methods: The primary components of the system are inertial measurement units, which track the human movement without spatial limitations, and an augmented reality headset used to project the visual feedback in the visual field. The adapted gait protocol contained five different target angles ranging from 15 degrees toe-out to 5 degrees toe-in. Eleven healthy participants walked on an instrumented treadmill, and the protocol was performed using both an established laboratory visual feedback driven by optical motion capture, and the proposed wearable system. Results and conclusions: The wearable system tracked FPA with an accuracy of 2.4 degrees RMS and ICC=0.94 across all target angles and subjects, when compared to an optical motion capture reference. In addition, the effectiveness of the biofeedback, reflected by the number of steps with FPA value ±2 degrees from the target, was found to be around 50% in both wearable and laboratory approaches. These findings demonstrate that retraining of the FPA using wearable inertial sensing and visual feedback is feasible with effectiveness matching closely an established laboratory method. The proposed wearable setup may reduce the complexity of gait retraining applications and facilitate their transfer to routine clinical practice.@en

Inertial sensors are used in an increasing number of autonomous applications. Integrating such sensors into dynamic systems, the problem of their calibration arises naturally. Existing methods often require the sensor to be accurately placed in certain poses, which can be infeasible in practice. In this paper, we present an optimization-based estimator for in-field identification of inertial biases and scale factors. Instead of predefined poses, we use measurements of an accurate global navigation satellite system receiver in the calibration algorithm. By adopting a moving horizon scheme, the resulting estimator has the potential to run on embedded hardware allowing for online calibration without sacrificing robustness. We also present an approach for the simulation of realistic sensor data. The resulting datasets are used to analyze the performance of the optimization-based estimator. The evaluated statistics clearly show that moving horizon estimation improves the robustness and accuracy of the presented calibration approach in the presence of uncertain initial conditions and outperforms traditional recursive filters.@en