2019 Theses Doctoral
Trunk Rehabilitation Using Cable-Driven Robotic Systems
Upper body control is required to complete many daily tasks. One needs to stabilize the head and trunk over the pelvis, as one shifts the center of mass to interact with the world. While healthy individuals can perform activities that require leaning, reaching, and grasping readily, those with neurological and musculoskeletal disorders present with control deficits. These deficits can lead to difficulty in shifting the body center of mass away from the stable midline, leading to functional limitations and a decline in the quality of activity. Often these patient groups use canes, walkers, and wheelchairs for support, leading to occasional strapping or joint locking of the body for trunk stabilization.
Current rehabilitation strategies focus on isolated components of stability. This includes strengthening, isometric exercises, hand-eye coordination tasks, isolated movement, and proprioceptive training. Although all these components are evidence based and directly correlate to better stability, motor learning theories such as those by Nikolai Bernstein, suggest that task and context specific training can lead to better outcomes. In specific, based on our experimentation, we believe functional postural exploration, while encompassing aspects of strengthening, hand-eye coordination, and proprioceptive feedback can provide better results.
In this work, we present two novel cable robotic platforms for seated and standing posture training. The Trunk Support Trainer (TruST) is a platform for seated posture rehabilitation that provides controlled external wrench on the human trunk in any direction in real-time. The Stand Trainer is a platform for standing posture rehabilitation that can control the trunk, pelvis, and knees, simultaneously. The system works through the use of novel force-field algorithms that are modular and user-specific. The control uses an assist-as-needed strategy to apply forces on the user during regions of postural instability. The device also allows perturbations for postural reactive training.
We have conducted several studies using healthy adult populations and pilot studies on patient groups including cerebral palsy, cerebellar ataxia, and spinal cord injury. We propose new training methods that incorporate motor learning theory and objective interventions for improving posture control. We identify novel methods to characterize posture in form of the “8-point star test”. This is to assess the postural workspace. We also demonstrate novel methods for functional training of posture and balance.
Our results show that training with our robotic platforms can change the trunk kinematics. Specifically, healthy adults are able to translate the trunk further and rotate the trunk more anteriorly in the seated position. In the standing position, they can alter their reach strategy to maintain the upper trunk more vertically while reaching. Similarly, Cerebral Palsy patients improve their trunk translations, reaching workspace, and maintain a more vertical posture after training, in the seated position. Our results also showed that an Ataxia patient was able to improve their reaching workspace and trunk translations in the standing position. Finally, our results show that the robotic platforms can successfully reduce trunk and pelvis sway in spinal cord injury patients. The results of the pilot studies suggest that training with our robotic platforms and methods is beneficial in improving trunk control.
This item is currently under embargo. It will be available starting 2020-08-21.
More About This Work
- Academic Units
- Mechanical Engineering
- Thesis Advisors
- Agrawal, Sunil K.
- Ph.D., Columbia University
- Published Here
- October 3, 2019