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Research Papers

Muscle Tension Estimation in the Presence of Neuromuscular Impairment

[+] Author and Article Information
José Zariffa

 International Collaboration On Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, V5Z 1M9, Canada; Department of Computer Science,  University of British Columbia, Vancouver, BC, V6T 1Z4, Canada

John D. Steeves

 International Collaboration On Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, V5Z 1M9, Canada

Dinesh K. Pai1

 International Collaboration On Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, V5Z 1M9, Canada; Department of Computer Science,  University of British Columbia, Vancouver, BC, V6T 1Z4, Canadapai@cs.ubc.ca

1

Please send correspondence to Dr. Dinesh K. Pai. Present address: Department of Computer Science, University of British Columbia, 201-2366 Main Mall, Vancouver, BC, V6T 1Z4, Canada.

J Biomech Eng 133(12), 121009 (Dec 28, 2011) (9 pages) doi:10.1115/1.4005483 History: Received August 24, 2011; Revised November 30, 2011; Published December 28, 2011; Online December 28, 2011

Static optimization approaches to estimating muscle tensions rely on the assumption that the muscle activity pattern is in some sense optimal. However, in the case of individuals with a neuromuscular impairment, this assumption is likely not to hold true. We present an approach to muscle tension estimation that does not rely on any optimality assumptions. First, the nature of the impairment is estimated by reformulating the relationship between the muscle tensions and the external forces produced in terms of the deviation from the expected activation in the unimpaired case. This formulation allows the information from several force production tasks to be treated as a single coupled system. In a second step, the identified impairments are used to obtain a novel cost function for the muscle tension estimation task. In a simulation study of the index finger, the proposed method resulted in muscle tension errors with a mean norm of 23.3 ± 26.8% (percentage of the true solution norm), compared to 52.6 ± 24.8% when solving the estimation task using a cost function consisting of the sum of squared muscle stresses. Performance was also examined as a function of the amount of error in the kinematic and muscle Jacobians and found to remain superior to the performance of the squared muscle stress cost function throughout the range examined.

FIGURES IN THIS ARTICLE
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Copyright © 2011 by American Society of Mechanical Engineers
Topics: Muscle , Tension , Force , Errors
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References

Figures

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Figure 5

Muscle tension estimation in four tasks when FDSI and FDPI are at half strength (impairment level = 0.5)

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Figure 6

Impairment estimation errors (top plot) and tension estimation errors (bottom plot) as a function of ε (defined in Sec. 2)

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Figure 7

(a) Layout of the system of equation for impairment estimation when all tasks are uncoupled. In this example, there are two tasks, A and B, each with a torque difference vector ΔτX , a muscle tension difference vector Δξ̂X , a Jacobian MX , and a Jacobian normalization matrix SX (as described in Eq. 3). MX depends only on the finger position and, therefore, may be the same for several tasks if the finger position is the same. (b) Layout of the system if every muscle is coupled across tasks. The vector Δξ̂A+B is common to both tasks and contains one variable per muscle. (c) Example of a system with partial coupling. In this example, only the first three muscles are coupled across tasks. The numerical subscripts denote columns in the case of a matrix and entries in the case of a vector.

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Figure 4

Muscle tension estimation in four tasks when EDCI and EIP are fully impaired

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Figure 1

Flow chart showing the key steps of the impairment estimation and muscle tension estimation processes. Corresponding variables in the text are indicated. Orange boxes indicate quantities obtained from population data, green boxes indicate directly measured quantities, and blue boxes indicate computed quantities.

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Figure 2

Deviation DM in four tasks when EDCI and EIP are fully impaired. In this scenario, EDCI and EIP are coupled across tasks (indicated by box), and therefore can be seen to have equal deviation values in all tasks. The deviation values for the coupled muscles reflect the impairment present. The deviation values of the uncoupled muscles (here, FDPI, FDSI, FDI, and FPI) reflect task-specific changes in the activation requirements of unimpaired muscles as a result of the impairment.

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Figure 3

Deviation DM in four tasks when FDSI and FDPI are at half strength (impairment level = 0.5). In this scenario, FDPI, FDSI, and FDI are coupled across tasks (indicated by box) and, therefore, can be seen to have equal impairment values in all tasks. The deviation values for the coupled muscles reflect the impairment present. The deviation values of the uncoupled muscles (here, EDCI, EIP, and FPI) reflect task-specific changes in the activation requirements of unimpaired muscles as a result of the impairment.

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