Abstract
Slacking limits the rehabilitative effectiveness of certain exercises following stroke. When patients receive assistance during an exercise, they exhibit a persistent tendency to reduce their own contribution to that exercise. This phenomenon was first coined 'slacking' in the context of robot-mediated therapy, where controller design continues to involve prediction and mitigation of slacking. In this pilot study, 14 individuals in the chronic stage of stroke participated in a visuomotor tracking task during which they produced isometric grip forces. Visual feedback displayed on a monitor helped participants track eight distinct forces ranging effort level from 4 to 30% maximum voluntary contraction (MVC). A specialized method of toggling between veridical and nonveridical visual feedback isolated each participant's realtime slacking rate at each of the eight effort levels, with both their contralesional and ipsilesional hand. Below 10-15% MVC, participants did not slack. At higher effort levels, participants slacked, and their slacking rate increased non-linearly with effort. Slacking took the form of smooth reductions in grip force. On average, across participants, slacking rates were remarkably similar between hands, just marginally faster with the contralesional hand. However, individualized slacking rates varied from almost zero to approximately double the acrossparticipant average. The paradigm for measuring slacking rate, used here, might be incorporated into robot-mediated therapy to maintain an accurate, individualized estimate of a patient's slacking rate at various force levels and ensure the robot provides assistance only as needed.
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