North Carolina State University
Robotic lower limb exoskeletons aim to improve or augment limb functions. Automatic modulation of robotic assistance is very important because it can increase the assistive outcomes and guarantee safety when using exoskeletons. However, this automatic assistance adjustment is challenging due to person-to-person and day-to-day variations, as well as the time-varying complex human-machine-interaction forces. In recent years, human-in-the-loop optimization methods have been investigated to reduce participants' metabolic costs by providing personalized assistance from robotic exoskeletons. However, metabolic cost measure is noisy and the experimental protocol is usually relatively long. In addition, the influence of exoskeleton control on this human state in terms of energetic cost is unclear and indirect. More importantly, the optimization by reducing metabolic cost is found to affect human gait patterns and cause undesired outcomes. In this study, new evaluation measures other than metabolic cost will be investigated to optimize the assistance from a powered hip exoskeleton based on a reinforcement learning method. It is hypothesized that the new reinforcement learning-based optimal control approach will produce personalized torque assistance, reduce human volitional effort, and improve balance and other performance during walking tasks. Both participants without and with neurological disorders will be included in this study.
This research is primarily being done in the NCSU/UNC Joint Department of Biomedical Engineering at North Carolina State University (NCSU) and is also collaborated with the University of North Carolina at Chapel Hill (UNC-Chapel Hill). NCSU will have the following roles in this study: (1) new research and development of controls algorithms, (2) conducting experiments, (3) recruitment, (4) data collection, (5) the consent process, (6) handling identifiable information (consent paperwork, photos and videos with the subject's face). Dr. Michael Lewek at UNC-Chapel Hill (also serves as a physical…
Inclusion Criteria for non-neurologically affected populations: * Between 18 and 64 years old * Live in the United States * Able to understand study requirements and sign an informed consent * Have full range of motion in your hip joint * Able to walk normally without any assistance. Exclusion Criteria for non-neurologically affected populations: * Cannot follow instructions or provide feedback due to cognitive or language limitations * Suffered from a stroke that affects balance or walking * Use an electronically controlled medical device, such as a pacemaker, implanted defibrillator, or d…
The bilateral hip exoskeleton has two degrees of freedom to enable the hip joint extension and flexion movement on both left and right sides. The zero impedance mode will not provide any assistance or resistance to the hip joints.
The personalized optimal assistance mode includes both individualized hip flexion and hip extension assistance, which is determined by using the reinforcement learning-based automatic control parameters tuning during walking tasks. Therefore, the personalized optimal assistance will be able to improve the walking gait performance and reduce the energetic consumption.
The free walking mode will not include the usage of the wearable bilateral hip exoskeleton, and the human walking subjects will conduct pure natural walking tasks.