Dynamic postural control during sitting is essential for functional mobility and daily activities. Extended reality (XR) presents a promising solution for posture training in addressing conventional training limitations related to patient accessibility and ecological validity. We developed a remote XR rehabilitation system with markerless motion tracking for sitting posture training. Forty-two healthy subjects participated in this proof-ofconcept pilot study. Each subject completed 24 rounds of multidirectional reach tasks using the system and 24 rounds without it. Motion data were collected via Zoom meetings using built-in cameras in laptops. Functional reach test scores were analyzed to assess the impact of the system on motor performance. Four standard questionnaires were used to assess the effects of this system on presence, simulator sickness, engagement, and enjoyment. Our results indicate that the remote XR training system significantly improved functional reach performance and proved highly effective for telerehabilitation. XR interaction also enhanced training engagement and enjoyment. By bridging the spatial gap between patients and therapists, this system enables personalized and engaging home-based intervention. Additionally, it facilitates more natural movements by eliminating body marker constraints and laboratory limitations. This study should serve as a stepping stone to advancing novel remote XR rehabilitation systems.

Virtual reality (VR) and augmented reality (AR) are emerging technologies in rehabilitation and have the potential to be combined with robot-assisted training (RAT). In this study, we investigate the effects of adding VR and AR into sitting posture training involving a robotic Trunk Support Trainer (TruST). Sixty-three healthy subjects were randomly assigned to three groups: physical reality group (PR group), AR group, and VR group. During training, subjects practice multidirectional reach tasks with robotic assistance provided by TruST. Reach targets were real in the PR group but virtual in the AR and VR groups. Training environments were real in the PR and AR groups, but virtual in the VR group. Before and after training, all subjects underwent a functional reach test to measure changes in motor performance, gains in workspace area and postural control flexibility. Additionally, they completed five standard questionnaires assessing presence, immersion, simulator sickness, engagement, and enjoyment. Our results indicate that both VR and AR significantly enhanced the effectiveness of TruST-assisted training. However, the VR group experienced a higher simulator sickness compared to the AR group. This comparative study sheds light on the added value of VR and AR in RAT and should serve as a stepping stone for the development of novel XR-enhanced RAT platforms and paradigms for training.

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.