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These 'Bots Are Made for Walking

Stephen Piazza

Strategies for Robot Therapy

2013-09TechnoPiazzaF2.jpgClick to Enlarge ImageThose preliminary evaluations probably understate the potential of robot-assisted therapy, perhaps significantly, because researchers do not yet have a clear idea of the best way for a robot to interact with a patient’s legs. Robots have made impressive inroads in industry because of their capacity to perform the same tasks over and over with high precision, and specialists in robot rehabilitation initially employed those machines the same way: Robots would guide patients as they moved their legs on the treadmill, with the exoskeleton providing consistent corrective assistance when its sensors detected the patient deviating from a predetermined normal gait pattern. The end result is that the patients’ legs would always move normally, with the patients doing what they could with their muscles, and the exoskeleton making up the difference with its motors.

That approach, called position control, is an excellent way to program a robot tasked with machining identical engine parts on an assembly line, but it may not be ideal for helping people relearn to walk. True, moving the patient through a normal walking motion would show him or her what such movements feel like, and it might even generate sensory signals in the patient’s own nerves that would be helpful in relearning gait. Robot-guided motions would be preferable to no motion at all for patients with a severe disability. On the other hand, position control may rob patients of the motivation to generate normal motions because the robot produces all the movement patterns for them.

David Reinkensmeyer of the University of California at Irvine calls that possible loss of motivation the “slacking hypothesis.” When a robot guides limb motions, the patient’s body may adapt automatically by minimizing energy expenditure. Such compensation presents a problem, Reinkensmeyer says, because the rehabilitation patient needs to expend effort to learn how to walk again.

If you have ever taught a child to ride a bike, you know that when a motor skill is first learned, there is considerable fluctuation in the movement. But does that variation represent deviation that the nervous system will attempt to eliminate, or does it actually aid in learning? Russian neurophysiologist Nikolai Bernstein theorized that movement variation allows the nervous system to practice solving the problem of planning motion, in much the same way that a fourth-grader develops computational skills by completing a worksheet of many different long division problems. “Repetition without repetition,” according to Bernstein, is the key to motor learning.

The challenge, then, is to find the best way for the robot to allow the patient to deviate from perfect repetition. To that end, several groups are now exploring an alternative to position control, called impedance control. Mechanical impedance is resistance to motion; the idea behind impedance control is that the robot would simulate a springlike resistance to deviation from the desired movement trajectory. The spring might be very soft or even nonexistent for small deviations, giving patients the opportunity to learn how to correct their own errors. Larger deviations would be met with greater resistance that would move the patient along the preferred path and perhaps prevent him or her from becoming frustrated early in the course of rehabilitation.

Robots that guide patient motions in this way may behave much like the way our own limbs respond when we encounter a disturbance, such as when someone jostles your arm while you reach for a cup of coffee. In a 2004 study, kinesiologists David Franklin and Theodore Milner of Simon Fraser University in Canada, along with Udell So and Mitsuo Kawato of the Advanced Telecommunications Research Institute in Kyoto, asked healthy people to perform reaching movements while gripping a handle attached to a robot arm that attempted to destabilize their motion. Test subjects were still able to make straight movements by modifying the springlike behavior of their own limbs: They stiffened their arms in the direction perpendicular to the path of their reach, but not in the movement direction.

A similar strategy underlies a variant of impedance control called path control. In this approach, the robot produces less resistance along the desired line of movement so that patients have more control over the timing of their motions. The legs are therefore free to move when the patient propels them in the right direction, but the robot applies a gentle correction when they stray too far from the prescribed path, creating virtual “walls” that prevent too much sideways deviation.

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