In their daily lives, stroke survivors must often choose between attempting upper-extremity activities using their impaired limb, or compensating with their less impaired limb. Choosing their impaired limb can be difficult and discouraging, but might elicit beneficial neuroplasticity that further reduces motor impairments, a phenomenon referred to as “the virtuous cycle”. In contrast, compensation is often quicker, easier, and more effective, but can reinforce maladaptive changes that limit motor recovery, a phenomenon referred to as “learned non-use”. This dissertation evaluated the role of robotic assistance in, and designed a wearable sensing system for, promoting the virtuous cycle.
In the first half of the dissertation, we use the FINGER robot to test the hypothesis that robotic assistance during clinical movement training triggers the virtual cycle. FINGER consists of two singly-actuated mechanisms that assist individuated movement of the index and middle fingers. 30 chronic stroke participants trained in FINGER using a GuitarHero-like game for nine sessions. Half were guided by an adaptive impedance controller towards a success rate of 85%, while the other half were guided towards 50%. Increasing assistance to enable successful practice decreased effort, but primarily for less-impaired participants. Overall, however, high success practice was as effective (or more) as low success practice and even more effective for highly impaired individuals. Participants who received high assistance training were more motivated and reported using their impaired hand more at home. These results support the hypothesis that high assistance clinical movement training motivates impaired hand use, leading to greater use of the hand in daily life, resulting in a self-training effect that reduces motor impairment.
The second half of the dissertation describes the development of the manumeter - a non-obtrusive wearable device for monitoring and incentivizing impaired hand use. Contrasted against wrist accelerometry (the most comparable technology), the manumeter uses a magnetic ring and a wristband with mangetometers to detect wrist and finger movement rather than gross arm movement. We describe 1) the inference of wrist and finger movement from differential magnetometer readings using a radial basis function network, 2) initial testing in which distance traveled estimates were within 94.7%±19.3 of their goniometricly measured values, 3) experiments with non-impaired participants in which the manumeter detected some functional activities better than wrist accelerometry, and 4) improvements to the hardware and data processing that allow both subject-independent tracking of the position of the finger relative to the wrist (RMS errors < 1cm) and highly reliable detection of whether the hand is open or closed. Its performance and non-obtrusive design make the manumeter well suited for measuring and reinforcing impaired hand use in daily life after stroke.
The contributions of this dissertation are experimental confirmation that high assistance movement training promotes the virtuous cycle, and development of a wearable sensor for monitoring hand movement in daily life. Training with robotic assistance and hand use feedback may ultimately help individuals with stroke recover to their full potential.
|Advisor:||Reinkensmeyer, David J.|
|Commitee:||Cramer, Steven C., Nenadic, Zoran|
|School:||University of California, Irvine|
|Department:||Biomedical Engineering - Ph.D.|
|School Location:||United States -- California|
|Source:||DAI-B 77/02(E), Dissertation Abstracts International|
|Keywords:||Accelerometry, Magnetic sensing, Rehabilitation robotics, Stroke, Wearable sensors|
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