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Elizabeth B. Brokaw, MS; Theresa Murray, BS; Tobias Nef, PhD; Peter S. Lum, PhD

This study focused on developing a haptic-based approach, Time-Independent Functional Training (TIFT), using ARMin III robotic exoskeleton to retrain interjoint arm coordination post-stroke. The results showed significant improvement in reducing errors and guiding torque with TIFT compared to time-dependent training. While all training groups showed error reduction in movement recall, TIFT's unique advantages and promising outcomes call for further research. TIFT minimally interferes with muscle activation, promotes proper coordination, and requires kinematic variability, leading to reduced interaction forces and improved movement quality.

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Elizabeth B. Brokaw, MS; Theresa Murray, BS; Tobias Nef, PhD; Peter S. Lum, PhD

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  1. Retraining of interjoint arm coordination after stroke using robot-assistedtime-independent functional training Elizabeth B. Brokaw, MS; Theresa Murray, BS; Tobias Nef, PhD; Peter S. Lum, PhD

  2. Study aim • Develop time-independent functional training (TIFT), a haptic-based approach for retraining interjoint coordination poststroke. • Implement TIFT in ARMin III robotic exoskeleton. • Relevance: • Abnormal interjoint coordination is common after stroke. • Recovery is possible with focused intervention that inhibits compensatory strategies and promotes learning of proper interjoint coordination during reaching.

  3. Motor Learning Testing • 37 nondisabled subjects • 3 training groups: TIFT, visual demonstration, and time-dependent (TD) training. • Performed 8 blocks of 10 repetitions of task training with recall testing and 1 min of rest between each block. ARMin III robot and passive hand device, HandSOME, being used in functional shelf task.

  4. Training Results • TIFT subjects signifi-cantly reduced errors in training (p < 0.001) but TD subjects did not (p = 0.76). • Robot guidance torque decreased significantly across training blocks in TIFT (p < 0.001) but not TD (p = 0.67). Typical subject’s joint coordination pattern during TIFT training. Trajectories were disjointed in 1st training block (top)but smoother by 8th training block (bottom).

  5. Movement Recall Results • All three groups reduced error across movement recall blocks (p < 0.001). • Same observation for reduction of slope error (p = 0.018) and movement variability (p < 0.001). • However, no significant between-group differences for any metrics (p > 0.20). Error reductions during recall blocks with standard error bars.

  6. Conclusions • Can not yet recommend TIFT over more easily implemented TD, but TIFT warrants further study: • Theoretical advantages: • Minimally interferes with input/output map between correct muscle activation and movement. • Allows greater kinematic variability. • Requires subjects to produce proper interjoint coordination to advance. • Training advantages • Lower interaction forces between robot and human arm (thus arm contributing more to movement during TIFT). • Error and assistance forces reduced during TIFT but not TD.

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