A Portable Device for Quantification of Forearm Muscle Tone

摘要

The ability to objectively quantify muscle tone is important in the assessment of the effectiveness of therapeutic intervention in persons with spasticity and other conditions associated with upper motor neuron disorders. Spasticity is defined as a velocity-dependent hypertonia caused by an abnormally high involuntary contraction of a muscle or group of muscles due to a rate-dependent reflex mechanism [1, 2]. Tone, or resistance to external stretch, can be modeled mathematically with the use of viscoelastic parameters when one measures the torque and displacement of a perturbation. In this brief report, we describe the design and initial implementation of a novel direct-drive robotic device that provides a wide range of displacement trajectories for the identification of limb viscoelastic parameters. Current assessments of tone, such as the Ashworth Scale [3], are subjective and qualitative in nature and have been shown to be unreliable [4, 5]. New methods are needed. [2, 5-7]. Although electromyography (EMG) measurements are useful for monitoring the timing of reflex responses tests, they are not ideal for quantifying muscle tone because EMG signals can be extremely “noisy.” Although electromyography (EMG) measurements are useful for monitoring the timing of reflex responses, they are not ideal for quantifying muscle tone, because EMG signals can be extremely “noisy.” EMG magnitude is correlated with the active force produced by the muscle. However, the exact relationship is complex and can vary among subjects due to electrode placement and the nature of the contraction [5, 7], particularly when the magnitude of the noise is similar to that of the EMG. An electromechanical direct-drive system was constructed to generate arbitrary trajectories of variable position and velocity profiles in which the torque, speed, and direction of the actuator (motor) constantly vary. Smooth random trajectories were generated in software with the use of Matlab 6.5 (Mathworks Inc, Natick, MA). The encoder output signal is provided to both the motion controller (Galil Motion Control, Rocklin, CA) for feedback control and to a data acquisition card (National Instruments, Austin, TX), and thus position data were acquired along with the experimental torque and EMG data. Because the output shaft is perpendicular to the ground, gravity has no influence on the torque (Figure 1). The system was automated with software written in LabVIEW 5.0 (National Instruments, Austin, TX) in conjunction with DMC code (Galil Motion Control, Rocklin, CA), which controls the motor. A detailed description of the device can be found at the following Web site: https://jshare.johnshopkins.edu/lwhitco1/papers/2011_Wristbot_Technical_Report.pdf. Person in position to use the “wristbot” (wrist-robot). After approval was obtained from the Johns Hopkins Institutional Review Board, all subjects provided signed consent. Thirty-one control adults (10 men and 21 women) and 11 adults previously diagnosed with spasticity for at least 1 year (9 men and 2 women), ranging in age from 17 to 75 years, were tested. Subjects in the spastic group, a convenience sample, had experienced either traumatic brain injury or stroke. Each subject was seated in front of the robotic device as shown in Figure 1 and was instructed to relax the entire forearm and not resist the robot's movement. Although EMG data were not used in the quantification of muscle tone, data acquired from trials that contained any irregular EMG voltage signals thought to be attributed to voluntary muscle contractions were discarded and rerun. Each of the 10 trials contained 20 seconds of sample data. With each experiment, the mean of each of the 4 parameters (elastic stiffness, viscosity, rotational inertia, and torque offset) was calculated across the 10 trials. The average and standard error of these values for each group were calculated. Paired t-tests were used to assess within-subject differences for the control group. Prep