A document by Xupeng Ai . Click on the document to view its contents.

A document by Xupeng Ai . Click on the document to view its contents.

This articles discusses the use of the Robotic Upright Stand Trainer (RobUST), a cable driven exoskeleton that actuates the trunk and pelvis independently, for postural balance control training. It examines the changes that occur in healthy patients after a perturbation based balance training session conducted in virtual reality. In addition, there are two testing conditions tested. In one condition subjects are given assistive support at the pelvis by RobUST to help resist perturbations and in the other, there is no assistive support. Data collected include electromyographic data from lower limb muscles, movement and variability of movement data.

Seated postural limit defines the region from beyond which a subject cannot return the trunk to the neutral position without additional external support. The seated postural limits can be used as a reference to provide assistive support to the torso by an appropriately designed robotics, e.g., Trunk Support Trainer (TruST). However, fixed boundary representations of seated postural limits in the literature cannot capture the dynamically changing seated postural limits during training. In this study, we propose a conceptual model of the dynamic boundary of the trunk center using a boundary vector designed to track the postural-goal direction and trunk movement amplitude during a sitting task. We experimented with 20 healthy subjects. The results support our hypothesis that TruST intervention with an assist-as-needed controller based on the proposed dynamic boundary representation could achieve more significant sitting workspace area improvements than a fixed boundary representation. The second contribution of this paper is that we provide an approach to effectively introduce deep learning into TruST’s real-time controller design. We compiled a 3D trunk movement dataset which is currently the largest in the literature. We designed a loss capable of solving the gate-controlled regression problem. We proposed a novel roadmap for the exploration study. Following the roadmap, we developed a deep learning architecture, modified the widely used Inception module, and then obtained a deep learning model capable of accurately predicting the dynamic boundary in real-time. We believe this approach can be extended across other rehabilitation robots towards designing intelligent dynamic boundary-based assist-as-needed controllers.