Problem

Motor movement is the only method humans have of interacting with the environment, whether hailing a taxi cab or foraging for food. Indeed, all forms of sensory input and communication, including reading, writing, talking, and non-verbal gestures are mediated via the motor system. Upper-limb hypertonia, or colloquially spasticity, is a muscle control disorder that affects functional motor movement as well as muscle stiffness; it is observed in victims of stroke, cerebral palsy, multiple sclerosis, and other neurological deficits in the upper-extremity region. These abnormal neuro-muscular properties inhibit patients from executing proficient, goal-oriented tasks which constitute everyday life. Current literature studies quantifying spasticity report either inconsistent or clinically inapplicable results. Others validate measurements and analyses made with extremely subjective therapeutic scales-the Modified Ashworth Scale (MAS) being an example of one widely implemented practice. Moreover, an opportunity to construct a device that replaces these biased, inaccurate, and unreliable therapeutic ratings exists, as no current proposed apparatus has validated functionality with a universally accepted quantification scheme. The present study seeks to elucidate that quantifying spastic parameters using a robotic manipulandum and constructing a device by virtue of these findings is viable. Thereby, a clinically applicable, yet biomedically substantiated assessment method for measuring and understanding spasticity may be achievable. This would allow clinicians to more exactly track the progress and modality of spastic attributes, as well as significantly further understanding of the physiological mechanisms and movement manifestations that change in neurologically impaired survivors.

Objectives, Functions, & Requirements

Obtaining a quantitative evaluation of spasticity will require an understanding its relationship to measurable physiological systems, which will be accomplished using a robotic manipulandum. Collecting this data will help determine the appropriate parameters to measure, as well as the most effective methods with which to acquire these measurements. Ultimately, the goal is to create a widely-applicable and cost-effective device (with new or modified paradigm) capable of quantifying spasticity in a clinical setting. The results will be verified using research-based testing. In addition, the device should be easy to use and acquire the data in a safe manner.

The device will accomplish each of the following tasks:

  • passively move the patient’s arm, while maintaining a high level of comfort
  • utilize physiological signals (ie. torque, angular position, angular velocity, acceleration, EMG, transit time) to develop a quantitative scale for spasticity
  • provide feedback that can be easily understood and interpreted by rehabilitation therapists

The following provides a more specific list of objectives, along with the corresponding functions and requirements:

1. Accurate

    a. Measure position of catch

        i. Tap into servo potentiometer wire, use Arduino analog input (0-1023 range) to compute degree position

        ii. Compare time of max servo current draw to degree position to identify angle

2. Flexible

    a. Achieve variable velocity of motion

       i. Modify code from VarSpeedServo Arduino Library
       (https://github.com/netlabtoolkit/VarSpeedServo)

3. Safe

    a. Operate within safety bounds

       i. Employ a kill switch in servo power lead

       ii. Write code to terminate testing paradigm if excessive resistance is detected

4. Precise

    a. Restrict motion of upper arm

       i. Use padded support with Velcro straps over arm to elevate and hold upper arm in horizontal plane

5. Affordable

    a. Utilize off-the-shelf components

       i. Main servo unit comes as a preassembled gearbox package 
       (https://www.servocity.com/html/spg5685a-45_standard_rotation.html)

       ii. Tapping into the onboard potentiometer will yield usable angular position feedback

6. Verifiable Output

    a. Relate data from device testing to proven research

       i. Acquire data from patients with spasticity using INMOTION II robotic arm system; use this data to verify parameters which our device needs to measure

       ii. Statistical comparison of this data to data collected by our device will provide device validation

7. Practical/Usable

    a. Provide feedback to ensure user accessibility

       i. Create GUI in Matlab/Simulink to usefully display information regarding catch velocity and position, EMG data

8. Robust Sensor Array

    a. Transform measured variables to physiological signals via Arduino-Matlab code structure

       i. Derive relationships between power, transit time, and torque to relate measured to physiological signals

9. Durable

    a. Employ sufficiently strong materials to ensure mechanical soundness of device

       i. Forearm support structure should be 5/8” aluminum tubing, may use counterweight on rear side of joint to avoid overload on main gearbox joint bearing

10. Comfortable

    a. Use patient-friendly materials

       i. Any portion of the device coming into contact with skin should be allergen-free, provide sufficient padding against hard surfaces, and be clear of sharp edges