2022

IEEE Xplore

An Automatic Foot and Shank IMU Synchronization Algorithm: Proof-of-concept

Shaban Shabani, Alan K. Bourke, Amir Muaremi, Jens Praestgaard, Kate O’Keeffe, Rob Argent, Martin Brom, Celeste Scotti, Brian Caulfield, Lorcan C. Walsh

Novartis AG, Basel, Switzerland

Keywords

knee, legged locomotion, kinematics, sensor systems, robustness, kinetic theory, synchronization

Abstract

When using wearable sensors for measurement and analysis of human performance, it is often necessary to integrate and synchronise data from separate sensor systems. This paper describes a synchronization technique between IMUs attached to the shanks and insoles attached at the feet and aims to solve the need to compute the ankle joint angle, which relies on synchronized sensor data. This will additionally enable concurrent analysis using gait kinematic and kinetic features. A proof-of-concept of the algorithm, which relies on cross-correlation of gyroscope sensor data from the shank and foot, to align the sensor systems is demonstrated. The algorithm output is validated against those signals synchronized using manually annotated heel-strike and toe-off ground-truth signal landmarks, identified in both the shank and feet signals using previously published definitions. Results demonstrate that the developed algorithm is capable of synchronizing both sensor systems, based on IMU data from both healthy participants and participants suffering from knee osteoarthritis, with a mean lag time bias of 25.56ms when compared to the ground truth. A proof-of-concept of technique to synchronise IMUs attached to the shanks and insoles attached at the feet is demonstrated and offers an alternative approach to sensor system synchronisation.

Moticon's Summary

This paper introduces a synchronization technique for data from IMUs on the shanks and foot pressure insoles during gait analysis. The method uses cross-correlation of gyroscope data to align sensor systems, validated against manually annotated heel-strike and toe-off events. Moticon sensor insoles with integrated IMUs were used as part of the biomechanical sensing platform to measure gait parameters. The algorithm effectively synchronized sensor systems in both healthy participants and those with knee osteoarthritis, with a mean lag time bias of 25.56ms compared to the ground truth.

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