People can (re)learn faster and better with artificially enhanced error!
Stroke and related brain injuries give rise to multiple motor deficits that vary greatly by individual, including reaching errors, limited range of motion, and abnormal joint coupling (Dewald and Beer 2001). Training with robot-applied forces and visual feedback can stimulate neural reorganization to restore function. Studies using guidance-based robotic training have not revealed clear therapeutic advantages (Hornby, Reinkensmeyer et al. 2010). However, recent work has shown patient recovery from augmenting reality, either by augmenting the dynamics (Emken and Reinkensmeyer 2005), by augmenting error (Patton, Stoykov et al. 2006) or by augmenting exploratory movements (Huang and Patton 2011). The common feature of these methods is that they promote useful adaptation while encouraging independent activity. These approaches, however, still lack one powerful tactic commonly used in clinical practice — customizing treatment to each patient. We assert that this limitation is the reason interactive therapies have been ineffective for some patients.
Our long-term goal is to improve methods of therapy for neural injuries such as stroke. The objective of this proposal is to determine how statistical modeling of a patient’s motor deficits can be used to customize therapy. Recent work has shown how interactive machines can inform a direct mathematical relationship between patient deficits and applied interventions (Sundaram, Chen et al. 2011). Our approach first models the statistical tendencies of movement error, arising from deficits such as weakness, limited range of motion, spasticity and hemiparesis. Then, this model forms the structure of a “deficit-field”, the rules that drive the interactive forces and visual feedback during training. Our central hypothesis is that deficit-field training will accelerate recovery by focusing practice on the force-motion relationships associated with each deficit. This approach unifies and generalizes previous error augmentation methods to several applications of therapy, including goal-directed reaching (Aim 1), range of motion (Aim 2), and whole-body actions in functional tasks (Aim 3). Our rationale is that once deficit-field customization demonstrates advantages, the approach of linking treatment with statistical modeling of deficits will have a wide range of therapeutic applications.
This proposal evaluates deficit-field customization for training reaching accuracy, range of motion, and activities of daily living. We compare deficit-field training with conventional forms of augmented environments in 4-week clinical studies on chronic stroke survivors.