4.6 Article

Estimation of 3D Body Center of Mass Acceleration and Instantaneous Velocity from a Wearable Inertial Sensor Network in Transfemoral Amputee Gait: A Case Study

Journal

SENSORS
Volume 21, Issue 9, Pages -

Publisher

MDPI
DOI: 10.3390/s21093129

Keywords

sensor network; wearable sensors; gait analysis; lower-limb amputation; CoM; prosthesis; locomotion; MIMU; kinematics

Funding

  1. Universite Franco-Italienne [C2-881]

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A wearable framework based on MIMU networks for obtaining body center of mass acceleration and velocity was proposed and successfully validated in lower-limb amputees. Using multiple MIMUs on the trunk and both legs, as well as additional MIMUs on the thighs, resulted in high consistency and accuracy in estimating velocity and acceleration in different directions.
The analysis of the body center of mass (BCoM) 3D kinematics provides insights on crucial aspects of locomotion, especially in populations with gait impairment such as people with amputation. In this paper, a wearable framework based on the use of different magneto-inertial measurement unit (MIMU) networks is proposed to obtain both BCoM acceleration and velocity. The proposed framework was validated as a proof of concept in one transfemoral amputee against data from force plates (acceleration) and an optoelectronic system (acceleration and velocity). The impact in terms of estimation accuracy when using a sensor network rather than a single MIMU at trunk level was also investigated. The estimated velocity and acceleration reached a strong agreement (rho > 0.89) and good accuracy compared to reference data (normalized root mean square error (NRMSE) < 13.7%) in the anteroposterior and vertical directions when using three MIMUs on the trunk and both shanks and in all three directions when adding MIMUs on both thighs (rho > 0.89, NRMSE <= 14.0% in the mediolateral direction). Conversely, only the vertical component of the BCoM kinematics was accurately captured when considering a single MIMU. These results suggest that inertial sensor networks may represent a valid alternative to laboratory-based instruments for 3D BCoM kinematics quantification in lower-limb amputees.

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