Evaluation of force field training customized according to individual movement deficit patterns

Abstract

Variation in upper extremity motor impairments among stroke survivors creates challenges for the design of robot-assisted therapies. One approach to enhance treatment is to customize based on individual assessments of motor capabilities. However, current strategies are limited by the use of traditional assessments (e.g. Fugl-Meyer, goal-directed performance) for informing customization. Our approach characterizes natural motor behavior through distributions of self-directed motor exploration. We then design unique force fields that push participants towards their neglected movements in the velocity domain. In this study, we investigated how stroke survivors' (n = 6) movement patterns evolve with customized force field training and compared this to a control group that trained without forces (n = 6). Our results showed that both training groups improved Fugl-Meyer UE scores (2.5 ± 1.0 point and 1.5 ± 0.7 point improvements for the force field group and control group, respectively) and increased their movement capabilities in the velocity domain (104.1 ± 28.1% and 169.8 ± 101.4% increases for the force field group and control group, respectively). These results provide preliminary evidence that patient-specific force fields could be developed into a treatment that expands movement capabilities. To our knowledge, this study is the first to directly link distributions of movement to robot-assisted therapy.