Browsing by Author "Blumenschein, Laura H."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item A cable-based series elastic actuator with conduit sensor for wearable exoskeletons(IEEE, 2017) Blumenschein, Laura H.; McDonald, Craig G.; O’Malley, Marcia K.There is currently a scarcity of wearable robotic devices that can practically provide physical assistance in a range of real world activities. Soft wearable exoskeletons, or exosuits, have the potential to be more portable and less restrictive than their rigid counterparts. In this paper, we present the design of an actuation system that has been optimized for use in a soft exosuit for the human arm. The selected design comprises a DC motor and gearbox, a flexible cable conduit transmission, and a custom series elastic force sensor. Placed in series with the transmission conduit, the custom compliant force sensor consists of a translational steel compression spring with a pair of Hall effect sensors for measuring deflection. The custom sensor is validated as an accurate means of measuring cable tension, and it is shown that it can be used in feedback to control the cable tension with high bandwidth. The dynamic effect of the cable-conduit transmission on the force felt at the user interface is characterized by backdriving the system as it renders a range of virtual impedances to the user. We conclude with recommendations for the integration of such an actuation system into a full wearable exosuit.Item Improving short-term retention after robotic training by leveraging fixed-gain controllers(Sage, 2019) Losey, Dylan P.; Blumenschein, Laura H.; Clark, Janelle P.; O’Malley, Marcia K.Introduction: When developing control strategies for robotic rehabilitation, it is important that end-users who train with those strategies retain what they learn. Within the current state-of-the-art, however, it remains unclear what types of robotic controllers are best suited for promoting retention. In this work, we experimentally compare short-term retention in able-bodied end-users after training with two common types of robotic control strategies: fixed- and variable-gain controllers. Methods: Our approach is based on recent motor learning research, where reward signals are employed to reinforce the learning process. We extend this approach to now include robotic controllers, so that participants are trained with a robotic control strategy and auditory reward-based reinforcement on tasks of different difficulty. We then explore retention after the robotic feedback is removed. Results: Overall, our results indicate that fixed-gain control strategies better stabilize able-bodied users’ motor adaptation than either a no controller baseline or variable-gain strategy. When breaking these results down by task difficulty, we find that assistive and resistive fixed-gain controllers lead to better short-term retention on less challenging tasks but have opposite effects on the learning and forgetting rates. Conclusions: This suggests that we can improve short-term retention after robotic training with consistent controllers that match the task difficulty..