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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

Professor
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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References

Moeslund, T. B. , and Granum, E. , 2001, “ A Survey of Computer Vision-Based Human Motion Capture,” Comput. Vision Image Understanding, 81(3), pp. 231–268. [CrossRef]
Moeslund, T. B. , Hilton, A. , and Kru, V. , 2006, “ A Survey of Advances in Vision-Based Human Motion Capture and Analysis,” Comput. Vision Image Understanding, 104(2–3), pp. 90–126. [CrossRef]
Harvey, P. S. , and Gavin, H. , 2014, “ Assessing the Accuracy of Vision-Based Accelerometry,” Exp. Mech., 54(2), pp. 273–277. [CrossRef]
Leifer, J. , Weems, B. , Kienle, S. C. , and Sims, A. M. , 2011, “ Three-Dimensional Acceleration Measurement Using Videogrammetry Tracking Data,” Exp. Mech., 51(2), pp. 199–217. [CrossRef]
Gabler, L. F. , Crandall, J. R. , and Panzer, M. B. , 2016, “ Assessment of Kinematic Brain Injury Metrics for Predicting Strain Responses in Diverse Automotive Impact Conditions,” Ann. Biomed. Eng., 44(12), pp. 3705–3718. [CrossRef] [PubMed]
Ji, S. , Zhao, W. , Li, Z. , and McAllister, T. W. , 2014, “ Head Impact Accelerations for Brain Strain-Related Responses in Contact Sports: A Model-Based Investigation,” Biomech. Model. Mechanobiol., 13(5), pp. 1121–1136. [CrossRef] [PubMed]
Takhounts, E. G. , Ridella, S. A. , Rowson, S. , and Duma, S. M. , 2010, “ Kinematic Rotational Brain Injury Criterion (BRIC),” 22nd International Technical Conference on the Enhanced Safety of Vehicles, Washington, DC, June 13–16, pp. 1–10. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.454.2216&rep=rep1&type=pdf
Kleiven, S. , 2006, “ Evaluation of Head Injury Criteria Using a Finite Element Model Validated against Experiments on Localized Brain Motion, Intracerebral Acceleration, and Intracranial Pressure,” Int. J. Crashworthines, 11(1), pp. 65–79. [CrossRef]
Hernandez, F. , Wu, L. C. , Yip, M. C. , Laksari, K. , Hoffman, A. R. , Lopez, J. R. , Grant, G. , Kleiven, S. , and Camarillo, D. B. , 2015, “ Six Degree-of-Freedom Measurements of Human Mild Traumatic Brain Injury,” Ann. Biomed. Eng., 43(8), pp. 1918–1934. [CrossRef] [PubMed]
Naunheim, R. S. , Standeven, J. , Richter, C. , and Lewis, L. M. , 2000, “ Comparison of Impact Data in Hockey, Football, and Soccer,” J. Trauma, 48(5), pp. 938–941. [CrossRef] [PubMed]
Duma, S. M. , Manoogian, S. J. , Bussone, W. R. , Brolinson, P. G. , Goforth, M. W. , Donnenwerth, J. J. , Greenwald, R. M. , Chu, J. J. , and Crisco, J. J. , 2005, “ Analysis of Real-Time Head Accelerations in Collegiate Football Players,” Clin. J. Sports Med., 15(1), pp. 3–8.
Brolinson, P. G. , Manoogian, S. , McNeely, D. , Goforth, M. , Greenwald, R. , and Duma, S. , 2006, “ Analysis of Linear Head Accelerations From Collegiate Football Impacts,” Curr. Sports Med. Rep., 5(1), pp. 23–28. [CrossRef] [PubMed]
Crisco, J. J. , Wilcox, B. J. , Beckwith, J. G. , Chu, J. J. , Duhaime, A.-C. , Rowson, S. , Duma, S. M. , Maerlender, A. C. , McAllister, T. W. , and Greenwald, R. M. , 2011, “ Head Impact Exposure in Collegiate Football Players,” J. Biomech., 44(15), pp. 2673–2678. [CrossRef] [PubMed]
Mihalik, J. P. , Bell, D. R. , and Marshall, S. W. , 2007, “ Measurement of Head Impacts in Collegiate Football Players: An Investigation of Positional and Event—Type Differences,” Neurosurgery, 61(6), pp. 1229–1235. [CrossRef] [PubMed]
Bartsch, A. , Samorezov, S. , Benzel, E. , Miele, V. , and Brett, D. , 2014, “ Validation of an “Intelligent Mouthguard” Single Event Head Impact Dosimeter,” Stapp Car Crash J., 58, pp. 1–27. [PubMed]
Kang, Y.-S. , Moorhouse, K. , and Bolte, J. H. , 2011, “ Measurement of Six Degrees of Freedom Head Kinematics in Impact Conditions Employing Six Accelerometers and Three Angular Rate Sensors (6aω Configuration),” ASME J. Biomech. Eng., 133(11), p. 111007. [CrossRef]
Crisco, J. J. , Chu, J. J. , and Greenwald, R. M. , 2004, “ An Algorithm for Estimating Acceleration Magnitude and Impact Location Using Multiple Nonorthogonal Single-Axis Accelerometers,” ASME J. Biomech. Eng., 126(6), pp. 849–854.
Franck, J. A. , Blume, J. , Crisco, J. J. , and Franck, C. , 2015, “ Extracting Time-Accurate Acceleration Vectors From Nontrivial Accelerometer Arrangements,” ASME J. Biomech. Eng., 137(9), p. 091004. [CrossRef]
Zappa, B. , Legnani, G. , Van Den Bogert, A. J. , and Adamini, R. , 2001, “ On the Number and Placement of Accelerometers for Angular Velocity and Acceleration Determination,” ASME J. Dyn. Syst. Meas. Control, 123(3), pp. 552–553. [CrossRef]
Yoganandan, N. , Zhang, J. , Pintar, F. A. , and Liu, Y. K. , 2006, “ Lightweight Low-Profile Nine-Accelerometer Package to Obtain Head Angular Accelerations in Short-Duration Impacts,” J. Biomech., 39(7), pp. 1347–1354. [CrossRef] [PubMed]
Padgaonkar, A. J. , Krieger, K. , and King, A. , 1975, “ Measurement of Angular Acceleration of a Rigid Body Using Linear Accelerometers,” ASME J. Appl. Mech., 42(3), pp. 552–556. [CrossRef]
Campbell, K. R. , Warnica, M. J. , Levine, I. C. , Brooks, J. S. , Laing, A. C. , Burkhart, T. A. , and Dickey, J. P. , 2016, “ Laboratory Evaluation of the gForce Tracker, a Head Impact Kinematic Measuring Device for Use in Football Helmets,” Ann. Biomed. Eng., 44(4), pp. 1246–1256. [CrossRef] [PubMed]
Wu, L. C. , Nangia, V. , Bui, K. , Hammoor, B. , Kurt, M. , Hernandez, F. , Kuo, C. , and Camarillo, D. B. , 2016, “ In Vivo Evaluation of Wearable Head Impact Sensors,” Ann. Biomed. Eng., 44(4), pp. 1234–1245. [CrossRef] [PubMed]
Siegmund, G. P. , Guskiewicz, K. M. , Marshall, S. W. , DeMarco, A. L. , and Bonin, S. J. , 2016, “ Laboratory Validation of Two Wearable Sensor Systems for Measuring Head Impact Severity in Football Players,” Ann. Biomed. Eng., 44(4), pp. 1257–1274. [CrossRef] [PubMed]
Kuo, C. , Wu, L. C. , Hammoor, B. T. , Luck, J. F. , Cutcliffe, H. C. , Lynall, R. C. , Kait, J. R. , Campbell, K. R. , Mihalik, J. P. , Bass, C. R. , and Camarillo, D. B. , 2016, “ Effect of the Mandible on Mouthguard Measurements of Head Kinematics,” J. Biomech., 49(9), pp. 1845–1853. [CrossRef] [PubMed]
Camarillo, D. B. , Shull, P. B. , Mattson, J. , Shultz, R. , and Garza, D. , 2013, “ An Instrumented Mouthguard for Measuring Linear and Angular Head Impact Kinematics in American Football,” Ann. Biomed. Eng., 41(9), pp. 1939–1949. [CrossRef] [PubMed]
Jadischke, R. , Viano, D. C. , Dau, N. , King, A. I. , and McCarthy, J. , 2013, “ On the Accuracy of the Head Impact Telemetry (HIT) System Used in Football Helmets,” J. Biomech., 46(13), pp. 2310–2315. [CrossRef] [PubMed]
Foxlin, E. , 1996, “ Inertial Head-Tracker Sensor Fusion by a Complimentary Separate-Bias Kalman Filter,” IEEE Virtual Reality Annual International Symposium, Santa Clara, CA, Mar. 30–Apr. 3, p. 185.
Khaleghi, B. , Khamis, A. , Karray, F. O. , and Razavi, S. N. , 2013, “ Multisensor Data Fusion: A Review of the State-of-the-Art,” Inf. Fusion, 14(1), pp. 28–44. [CrossRef]
Sabatini, A. M. , 2006, “ Quaternion-Based Extended Kalman Filter for Determining Orientation by Inertial and Magnetic Sensing,” IEEE Trans. Biomed. Eng., 53(7), pp. 1346–1356. [CrossRef] [PubMed]
Zihajehzadeh, S. , Loh, D. , Lee, M. , Hoskinson, R. , and Park, E. J. , 2014, “ A Cascaded Two—Step Kalman Filter for Estimation of Human Body Segment Orientation Using MEMS—IMU,” 36th Annual International Conference of the IEEE Engineering in Medicince and Biology Society (EMBC), Chicago, IL, Aug. 26–30, pp. 6270–6273.
Won, S. H. P. , Melek, W. W. , and Golnaraghi, F. , 2010, “ A Kalman/Particle Filter-Based Position and Orientation Estimation Method Using a Position Sensor/Inertial Measurement Unit Hybrid System,” IEEE Trans. Ind. Electron., 57(5), pp. 1787–1798. [CrossRef]
Yun, X. , and Bachmann, E. , 2006, “ Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking,” IEEE Trans. Rob., 22(6), pp. 1216–1227. [CrossRef]
Luinge, H. J. , and Veltink, P. H. , 2005, “ Measuring Orientation of Human Body Segments Using Miniature Gyroscopes and Accelerometers,” Med. Biol. Eng. Comput., 43(2), pp. 273–282. [CrossRef] [PubMed]
Dissanayake, G. , Sukkarieh, S. , Nebot, E. , and Durrant-Whyte, H. , 2001, “ The Aiding of a Low-Cost Strapdown Inertial Measurement Unit Using Vehicle Model Constraints for Land Vehicle Applications,” IEEE Trans. Rob. Autom., 17(5), pp. 731–747. [CrossRef]
Roetenberg, D. , Luinge, H. J. , Baten, C. T. M. , and Veltink, P. H. , 2005, “ Compensation of Magnetic Disturbances Improves Inertial and Magnetic Sensing of Human Body Segment Orientation,” IEEE Trans. Neural Syst. Rehabil. Eng., 13(3), pp. 395–405. [CrossRef] [PubMed]
Kim, A. , and Golnaraghi, M. F. , 2004, “ Initial Calibration of an Inertial Measurement Unit Using an Optical Position Tracking System,” Position Location and Navigation Symposium (PLANS), Monterey, CA, Apr. 26–29, pp. 96–101.
Bancroft, J. B. , and Lachapelle, G. , 2011, “ Data Fusion Algorithms for Multiple Inertial Measurement Units,” Sensors, 11(7), pp. 6771–6798. [CrossRef] [PubMed]
SAE, 1995, “ Instrumentation for Impact Test—Part 1: Electronic Instrumentation,” SAE Paper No. SAE J211-1. https://www.sae.org/standards/content/j211/1_199503/
Abramowitz, M. , and Stequn, I. , 1965, Handbook of Mathematical Functions: With Formulas, Graphs, and Mathematical Tables, Vol. 55, Dover, Mineola, NY.
Hall, D. L. , and Llinas, J. , 1997, “ An Introduction to Multisensor Data Fusion,” Proc. IEEE, 85(1), pp. 6–23. [CrossRef]
Arulampalam, M. S. , Maskell, S. , Gordon, N. , and Clapp, T. , 2002, “ A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking,” IEEE Trans. Signal Process., 50(2), pp. 174–188. [CrossRef]
Hol, J. D. , Schön, T. B. , and Gustafsson, F. , 2006, “ On Resampling Algorithms for Particle Filters,” IEEE Nonlinear Statistical Signal Processing Workshop (NSSPW), Cambridge, UK, Sept. 11–15, pp. 79–82.
Douc, R. , Cappé, O. , and Moulines, E. , 2005, “ Comparison of Resampling Schemes for Particle Filtering,” Fourth International Symposium on Image and Signal Processing and Analysis (ISPA), Zagreb, Croatia, Sept. 15–17, pp. 64–69.
Wu, L. C. , Laksari, K. , Kuo, C. , Luck, J. F. , Kleiven, S. , Dale' Bass, C. R. , and Camarillo, D. B. , 2016, “ Bandwidth and Sample Rate Requirements for Wearable Head Impact Sensors,” J. Biomech., 39(13), pp. 2918–2924. [CrossRef]
Kuo, C. , Wu, L. , Zhao, W. , Fanton, M. , Ji, S. , and Camarillo, D. B. , 2017, “ Propagation of Errors From Skull Kinematic Measurements to Finite Element Tissue Responses,” Biomech. Modeling Mechanobiol., 17(1), pp. 235–247. [CrossRef]
Zhao, W. , Ford, J. C. , Flashman, L. A. , McAllister, T. W. , and Ji, S. , 2016, “ White Matter Injury Susceptibility Via Fiber Strain Evaluation Using Whole-Brain Tractography,” J. Neurotrauma, 33(20), pp. 1834–1847. [CrossRef] [PubMed]
Wan, E. A. , and Nelson, A. T. , 2001, “ Dual Extended Kalman Filter Methods,” Kalman Filtering Neural Networks, Wiley, New York, pp. 123–173. [CrossRef]

Figures

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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