Research Papers

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
e-mail: calvink@stanford.edu

Jake Sganga

Department of Bioengineering,
Stanford University,
Stanford, CA 94305
e-mail: sganga@stanford.edu

Michael Fanton

Department of Mechanical Engineering,
Stanford University,
Stanford, CA 94305
e-mail: mfanton@stanford.edu

David B. Camarillo

Department of Bioengineering,
Stanford University,
Stanford, CA 94305
e-mail: dcamarillo@stanford.edu

1Corresponding author.

Manuscript received August 28, 2017; final manuscript received March 30, 2018; published online May 24, 2018. Assoc. Editor: Barclay Morrison.

J Biomech Eng 140(9), 091006 (May 24, 2018) (11 pages) Paper No: BIO-17-1384; 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 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.

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Grahic Jump Location
Fig. 1

Instrumentation: (a) X2 skin patch and instrumented mouthguard, (b) representation of the X2 skin patch sensor network and reference instrumented mouthguard, and (c) image of the sensor network on a human subject

Grahic Jump Location
Fig. 2

Visual localization: (a) we placed checkerboards with known square sizes and obtained images of sensor pairs. Using the known checkerboard sizes, we then determined the relative translation and orientation between imaged sensor pairs, and (b) built a map of the entire sensor network. Note, while six sensors are used in the visual localization, only four sensors actively record impact data. The remaining two (placed on the cheeks) were positioned as intermediaries because the ear sensors and the eye sensors could not be reliably captured in a single image.

Grahic Jump Location
Fig. 3

Undetermined angular acceleration with collinear sensors: The observation process H matrix does not have rank 9 with collinear sensors because the reference frame can be rotated such that two components in the location vector rmg/s are equal. This results in an inability to determine the angular acceleration about the common line.

Grahic Jump Location
Fig. 4

Aggregated results show that particle filter with visual localization transforms has best accuracy: We assessed the accuracy of individual skin patch sensors and our methodologies in estimating (a) peak angular velocity magnitude, (b) peak angular acceleration magnitude, and (c) peak linear acceleration magnitude compared with the reference instrumented mouthguard. Using generic transforms gives significantly (p < 0.05) better estimates of linear and angular acceleration than individual sensors; however, using sensor transformations obtained from visual localization improves accuracy further. Significant improvement in angular velocity estimates is only obtained with visual localization, with the particle filter having significantly lower errors than all other methods. For each box and whisker, the central line represents the median, the box represents 25th and 75th percentiles, and the whiskers extend a maximum of 1.5 times the interquartile range with outliers represented by “+” symbols.

Grahic Jump Location
Fig. 5

Kinematics time history demonstrating oscillations in individual sensors smoothed by filter: Time histories of (a) angular velocity magnitude, (b) angular acceleration magnitude, and (c) linear acceleration magnitude as measured by the reference instrumented mouthguard, the individual skin patch sensors, and the best performing particle filter utilizing sensor transforms obtained from visual localization. While individual skin patch sensors exhibit large oscillations in angular velocity measurement (which are amplified through differentiation in angular acceleration and propagated through projection in linear acceleration), our methodology successfully accounts for individual sensor errors and returns a smooth estimate closely matching the ground truth measurement.

Grahic Jump Location
Fig. 6

Improved angular velocity accuracy when using more sensors: Our sensor fusion methodologies can accommodate any number of sensors; however, it requires at least three sensors for the rigid body formulation. We compared our visual localization sensor transforms and particle filter methodology when using the full four-sensor network against the minimal three-sensor network. We observed a significantly higher (p < 0.05) errors when using the minimal three-sensor network in estimating (a) peak angular velocity magnitude; however, no significant change in accuracy for (b) peak angular acceleration magnitude and (c) peak linear acceleration magnitude estimates.

Grahic Jump Location
Fig. 7

Coriolis term has negligible contribution in rigid body formulation: With rq/p=[0.1,0.1,0.1]cm, we compute the contribution of each term in the rigid body formulation (Eq. (1)). α×rq/p and the measured linear acceleration (ap) terms have similar magnitudes, while the Coriolis term (ω×(ω×rq/p)) is negligible in comparison. This indicates that the linearization of the rigid body formulation in the Kalman Filter may be sufficient for sensor network estimation of dynamic events.



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