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Technical Brief

An Engineering Model of Human Balance Control—Part I: Biomechanical Model

[+] Author and Article Information
Joseph E. Barton

Mem. ASME
Department of Veterans Affairs,
Maryland Exercise and Robotics Center of Excellence (MERCE),
Veterans Administration,
Maryland Health Care System,
Baltimore, MD 21201;
Departments of Neurology and Physical Therapy and Rehabilitation Science,
University of Maryland School of Medicine,
Baltimore, MD 21201
e-mail: jbarton@som.umaryland.edu

Anindo Roy

Department of Veterans Affairs,
Maryland Exercise and Robotics Center of Excellence (MERCE),
Veterans Administration,
Maryland Health Care System,
Baltimore, MD 21201;
Department of Neurology,
University of Maryland School of Medicine,
Baltimore, MD 21201;
Department of Bioengineering,
University of Maryland Clark School of Engineering,
College Park, MD 20742
e-mail: aroy@som.umaryland.edu

John D. Sorkin

Baltimore VA Medical Center,
Geriatric Research Education and Clinical Center (GRECC),
Baltimore, MD 21201;
Division of Gerontology and Geriatric Medicine,
University of Maryland School of Medicine,
Baltimore, MD 21201
e-mail: jsorkin@grecc.umaryland.edu

Mark W. Rogers

Department of Physical Therapy
and Rehabilitation Science,
University of Maryland School of Medicine,
Baltimore, MD 21201
e-mail: mrogers@som.umaryland.edu

Richard Macko

Department of Veterans Affairs,
Maryland Exercise and Robotics Center of Excellence (MERCE),
Maryland Health Care System,
Baltimore, MD 21201;
Department of Neurology,
University of Maryland School of Medicine,
Baltimore, MD 21201
e-mail: rmacko@grecc.umaryland.edu

1Corresponding author.

Manuscript received January 11, 2015; final manuscript received August 21, 2015; published online December 8, 2015. Assoc. Editor: Kenneth Fischer.This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.

J Biomech Eng 138(1), 014502 (Dec 08, 2015) (11 pages) Paper No: BIO-15-1009; doi: 10.1115/1.4031486 History: Received January 11, 2015; Revised August 21, 2015

We developed a balance measurement tool (the balanced reach test (BRT)) to assess standing balance while reaching and pointing to a target moving in three-dimensional space according to a sum-of-sines function. We also developed a three-dimensional, 13-segment biomechanical model to analyze performance in this task. Using kinematic and ground reaction force (GRF) data from the BRT, we performed an inverse dynamics analysis to compute the forces and torques applied at each of the joints during the course of a 90 s test. We also performed spectral analyses of each joint's force activations. We found that the joints act in a different but highly coordinated manner to accomplish the tracking task—with individual joints responding congruently to different portions of the target disk's frequency spectrum. The test and the model also identified clear differences between a young healthy subject (YHS), an older high fall risk (HFR) subject before participating in a balance training intervention; and in the older subject's performance after training (which improved to the point that his performance approached that of the young subject). This is the first phase of an effort to model the balance control system with sufficient physiological detail and complexity to accurately simulate the multisegmental control of balance during functional reach across the spectra of aging, medical, and neurological conditions that affect performance. Such a model would provide insight into the function and interaction of the biomechanical and neurophysiological elements making up this system; and system adaptations to changes in these elements' performance and capabilities.

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Figures

Grahic Jump Location
Fig. 1

Response to expected and unexpected balance disturbances

Grahic Jump Location
Fig. 2

Balance control system block diagram

Grahic Jump Location
Fig. 3

Thirteen-segment biomechanical model of standing balance (frontal view)

Grahic Jump Location
Fig. 4

In performing the BRT, subjects stood on force platforms, fixed gaze on disk, and pointed to it as it moved around screen. The locations of two landmarks on each foot (see text) were recorded during the test along with other kinematic data, and these were used to compute the perimeter of the BoS at each sampling instant. This computation is robust enough to accurately represent the perimeter should the subject shift their stance or momentarily raise up on the ball of one foot (as shown in the figure).

Grahic Jump Location
Fig. 5

(a) Normalized joint force (black solid lines) and disk position (gray dotted lines) versus time. (b) Frequency spectra of normalized force (black solid lines) and disk position (gray dotted lines). YHS.

Grahic Jump Location
Fig. 6

(a) Normalized joint force (black solid lines) and disk position (gray dotted lines) versus time. (b) Frequency spectra of normalized force (black solid lines) and disk position (gray dotted lines). Older HFR subject before balance training.

Grahic Jump Location
Fig. 7

(a) Normalized joint force (black solid lines) and disk position (gray dotted lines) versus time. (b) Frequency spectra of normalized force (black solid lines) and disk position (gray dotted lines). Older HFR subject after balance training.

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