Head Impact Kinematics Estimation With Network of Inertial Measurement Units

Author:

Kuo Calvin1,Sganga Jake2,Fanton Michael1,Camarillo David B.3

Affiliation:

1. Department of Mechanical Engineering, Stanford University, Stanford, CA 94305 e-mail:

2. Department of Bioengineering, Stanford University, Stanford, CA 94305 e-mail:

3. Professor Department of Bioengineering, Stanford University, Stanford, CA 94305 e-mail:

Abstract

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 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.

Funder

National Science Foundation

Office of Naval Research Global

Stanford Bio-X

Publisher

ASME International

Subject

Physiology (medical),Biomedical Engineering

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