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Research Papers

Simulating Acceleration From Stereophotogrammetry For Medical Device Design

[+] Author and Article Information
Philip A. Tresadern

Centre for Rehabilitation and Human Performance Research (CRHPR), Salford University, Salford M6 6PU, UKp.tresadern@salford.ac.uk

Sibylle B. Thies, Laurence P. J. Kenney, David Howard, Christine Smith

Centre for Rehabilitation and Human Performance Research (CRHPR), Salford University, Salford M6 6PU, UK

Julie Rigby

Salford Primary Care Trust, St. James’s House, Pendleton Way, Salford M6 5FW, UK

John Y. Goulermas

Department of Electrical Engineering and Electronics, University of Liverpool, Brownlow Hill, Liverpool L69 3GJ, UK

Although this is only an approximation to the true segment geometry, it is sufficient to investigate the effect of translating the sensor along the axis of the limb and rotating the sensor about the axis of the limb, as would occur if the sensor were located incorrectly during donning.

J Biomech Eng 131(6), 061002 (Apr 21, 2009) (9 pages) doi:10.1115/1.3118771 History: Received March 14, 2008; Revised March 13, 2009; Published April 21, 2009

Abstract

When designing a medical device based on lightweight accelerometers, the designer is faced with a number of questions in order to maximize performance while minimizing cost and complexity: Where should the inertial unit be located? How many units are required? How is performance affected if the unit is not correctly located during donning? One way to answer these questions is to use position data from a single trial, captured with a nonportable measurement system (e.g., stereophotogrammetry) to simulate measurements from multiple accelerometers at different locations on the body. In this paper, we undertake a thorough investigation into the applicability of these simulated acceleration signals via a series of interdependent experiments of increasing generality. We measured the dynamics of a reference coordinate frame using stereophotogrammetry over a number of trials. These dynamics were then used to simulate several “virtual” accelerometers at different points on the body segment. We then compared the simulated signals with those directly measured to evaluate the error under a number of conditions. Finally, we demonstrated an example of how simulated signals can be employed in a system design application. In the best case, we may expect an error of $0.028 m/s2$ between a derived virtual signal and that directly measured by an accelerometer. In practice, however, using centripetal and tangential acceleration terms (that are poorly estimated) results in an error that is an order of magnitude greater than the baseline. Furthermore, nonrigidity of the limb can increase error dramatically, although the effects can be reduced considerably via careful modeling. We conclude that using simulated signals has definite benefits when an appropriate model of the body segment is applied.

FIGURES IN THIS ARTICLE
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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

Figure 1

Overview of the virtual sensor concept. A RCF is tracked using stereophotogrammetry (indirectly, via a rigid cluster of markers); its dynamics are then transformed to a VCF.

Figure 2

Definition of the marker coordinate frame MCF based on positions of four markers, M1, M2, M3, and M4

Figure 3

Definition of the RCF when based on positions of four anatomical markers, R1, R2, R3, and R4

Figure 4

Frustum of cone used to model forearm. The distance between markers on the elbow specified the radius of the cone at the elbow, re. The radius at the wrist, rw, was defined similarly using wrist markers. The distance between the center of the elbow (midpoint of elbow markers) and the center of the wrist (midpoint of wrist markers) defined the length of the cone, L. The position and orientation of the VCF were then defined by these parameters plus two user-defined variables, l and θ.

Figure 5

Sketch of brace with xsens unit. The brace was designed so that it could be strapped to the subject’s arm in order to compare acceleration signals that were typical of those observed during human reaching movements. The rigidity of the brace ensured that the relative pose between the RCF and VCF remained constant at all times.

Figure 6

Sketch illustrating experimental setup without the rigid brace. Clusters are labeled as elbow, C1, C2, and wrist

Figure 7

Average error versus cut-off frequency when comparing all three pairs of signals. The nominal cut-off frequency of fc=6 Hz is shown as a vertical dotted line.

Figure 8

Error versus angular offset for an algorithm trained at a given location

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