Head Impact Kinematics Estimation with Network of Inertial Measurement Units

[+] Author and Article Information
Calvin Kuo

Department of Mechanical Engineering Stanford University Stanford, CA 94305

Jake A. Sganga

Department of Bioengineering Stanford University Stanford, CA 94305

Michael G. Fanton

Department of Mechanical Engineering Stanford University Stanford, CA 94305

David Camarillo

Professor, Department of Bioengineering Stanford University Stanford, CA 94305

1Corresponding author.

ASME doi:10.1115/1.4039987 History: Received August 28, 2017; Revised March 30, 2018


Wearable sensors embedded with inertial measurement units have become commonplace for the measurement of head impact biomechanics, but individual systems often suffer from a lack of measurement fidelity. While some researchers have focused on developing highly accurate, single sensor systems, we have taken a parallel approach in investigating optimal estimation techniques with multiple noisy sensors. In this work, we present a novel sensor network methodology that utilizes multiple skin patch sensors arranged on the head, and combines their data to obtain a more accurate estimate than any individual sensor in the network. Our methodology visually localizes subject-specific sensor transformations, and based on rigid body assumptions, applies estimation algorithms to obtain a minimum mean squared error estimate. During mild soccer headers, individual skin patch sensors had over 100% error in peak angular velocity magnitude, angular acceleration magnitude, and linear acceleration magnitude. However, when properly networked using our visual localization and estimation methodology, we obtained kinematic estimates with median errors below 20%. While we demonstrate this methodology with skin patch sensors in mild soccer head impacts, the formulation can be generally applied to any dynamic scenario, such as measurement of cadaver head impact dynamics using arbitrarily placed sensors.

Copyright (c) 2018 by ASME
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