Xupeng Ai

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-of-concept pilot study. Each subject completed 24 rounds of multi-directional reach tasks using the system and 24 rounds without it. Motion data were collected via online meetings using built-in camera in the user's laptop. 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.

Extended reality (XR) technology combines physical reality with computer synthetic virtuality to deliver immersive experience to users. Virtual reality (VR) and augmented reality (AR) are two subdomains within XR with different immersion levels. Both of these have the potential to be combined with robot-assisted training protocols to maximize postural control improvement. In this study, we conducted a randomized control experiment with sixty-three healthy subjects to compare the effectiveness of robot-assisted posture training combined with VR or AR against robotic training alone. A robotic Trunk Support Trainer (TruST) was employed to deliver assistive force at the trunk as subjects moved beyond the stability limits during training. Our results showed that both VR and AR significantly enhanced the training outcomes of the TruST intervention. However, the VR group experienced higher simulator sickness compared to the AR group, suggesting that AR is better suited for sitting posture training in conjunction with TruST intervention. Our findings highlight the added value of XR to robot-assisted training and provide novel insights into the differences between AR and VR when integrated into a robotic training protocol. In addition, we developed a custom XR application that suited well for TruST intervention requirements. Our approach can be extended to other studies to develop novel XR-enhanced robotic training platforms.

Spinal cord epidural stimulation can promote the recovery of motor function in individuals with severe spinal cord injury (SCI) by enabling the spinal circuitry to interpret sensory information and generate related neuromuscular responses. This approach enables the spinal cord to generate lower limb extension patterns during weight bearing, allowing individuals with SCI to achieve upright standing. We have shown that the human spinal cord can generate some standing postural responses during self-initiated body weight shifting. In this study, we investigated the ability of individuals with motor complete SCI receiving epidural stimulation to generate standing reactive postural responses after external perturbations were applied at the trunk. A cable-driven robotic device was used to provide constant assistance for pelvic control and to deliver precise trunk perturbations while participants used their hands to grasp onto handlebars for self-balance support (hands-on) as well as when participants were without support (free-hands). Five individuals with motor complete SCI receiving lumbosacral spinal cord epidural stimulation parameters specific for standing (Stand-scES) participated in this study. Trunk perturbations (average magnitude: 17 ± 3% body weight) were delivered randomly in the four cardinal directions. Participants attempted to control each perturbation such that upright standing was maintained and no additional external assistance was needed. Lower limb postural responses were generally more frequent, larger in magnitude, and appropriately modulated during the free-hands condition. This was associated with trunk displacement and lower limb loading modulation that were larger in the free-hands condition. Further, we observed discernible lower limb muscle synergies that were similar between the two perturbed standing conditions. These findings suggest that the human spinal circuitry involved in postural control retains the ability to generate meaningful lower limb postural responses after SCI when its excitability is properly modulated. Moreover, lower limb postural responses appear enhanced by a standing environment without upper limb stabilization that promotes afferent inputs associated with a larger modulation of ground reaction forces and trunk kinematics. These findings should be considered when developing future experimental frameworks aimed at studying upright postural control and activity-based recovery training protocols aimed at promoting neural plasticity and sensory-motor recovery.

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 study characterizes the effects of a postural training program on balance and muscle control strategies in a virtual reality (VR) environment. The Robotic Upright Stand Trainer (RobUST), which applies perturbative forces on the trunk and assistive forces on the pelvis, was used to deliver perturbation-based balance training (PBT) in a sample of 10 healthy participants. The VR task consisted of catching, aiming, and throwing a ball at a target. All participants received trunk perturbations during the VR task with forces tailored to the participant’s maximum tolerance. A subgroup of these participants additionally received assistive forces at the pelvis during training. Postural kinematics were calculated before and after RobUST training, including (i) maximum perturbation force tolerated, (ii) center of pressure (COP) and pelvic excursions, (iii) postural muscle activations (EMG), and (iv) postural control strategies (the ankle and hip strategies). We observed an improvement in the maximum perturbation force and postural stability area in both groups and decreases in muscle activity. The behavior of the two groups differed for perturbations in the posterior direction where the unassisted group moved towards greater use of the hip strategy. In addition, the assisted group changed towards a lower margin of stability and higher pelvic excursion. We show that training with force assistance leads to a reactive balance strategy that permits pelvic excursion but that is efficient at restoring balance from displaced positions while training without assistance leads to reactive balance strategies that restrain pelvic excursion. Patient populations can benefit from a platform that encourages greater use of their range of motion.

We tested twenty-one 6- to 10-month-old infants with a wide range of sitting experience in forward and rightward reaching during unsupported sitting on the floor. Sessions were video-recorded for further behavioral and machine learning-based kinematic analyses. All infants, including novice sitters, successfully touched and grasped toys in both directions. Infant falls, hand support, and base of support changes were rare. Infants with more sitting experience showed better upright posture than novice sitters. However, we found no differences in trunk displacement or reaching kinematics between directions or across sitting experience. Thus, multi-directional reaching is functional in both novice and experienced infant sitters. We suggest that trunk and arm stability in sagittal and frontal planes is integral to learning to sit.

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.

The boundary-based assist-as-needed (BAAN) force field is widely used in robotic rehabilitation and has shown promising results in improving trunk control and postural stability. However, the fundamental understanding of how the BAAN force field affects the neuromuscular control remains unclear. In this study, we investigate how the BAAN force field impacts muscle synergy in the lower limbs during standing posture training. We integrated virtual reality (VR) into a cable-driven Robotic Upright Stand Trainer (RobUST) to define a complex standing task that requires both reactive and voluntary dynamic postural control. Ten healthy subjects were randomly assigned to two groups. Each subject performed 100 trials of the standing task with or without assistance from the BAAN force field provided by RobUST. The BAAN force field significantly improved balance control and motor task performance. Our results also indicate that the BAAN force field reduced the total number of lower limb muscle synergies while concurrently increasing the synergy density (i.e., number of muscles recruited in each synergy) during both reactive and voluntary dynamic posture training. This pilot study provides fundamental insights into understanding the neuromuscular basis of the BAAN robotic rehabilitation strategy and its potential for clinical applications. In addition, we expanded the repertoire of training with RobUST that integrates both perturbation training and goal-oriented functional motor training within a single task. This approach can be extended to other rehabilitation robots and training approaches with them.

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.