Upper Limb work-related musculoskeletal disorders (WMSDs) present a significant health risk to industrial workers. To address this, rigid-body exoskeletons have been widely used in industrial settings to mitigate these risks while exosuits offer advantages such as reduced weight, lower inertia, and no need for precise joint alignment, However, they remain in the early stages of development, especially for reducing muscular effort in repetitive and forceful tasks like heavy lifting and overhead work. This study introduces a multiple degrees-of-freedom cable-driven upper limb bilateral exosuit for human power augmentation. Two control schemes were developed and compared: an IMU based controller, and a myoelectric controller to compensate for joint torque exerted by the wearer. The results of preliminary experiments showed a substantial reduction in muscular effort with the exosuit's assistance, with the myoelectric control scheme exhibiting reduced operational delay.
In parallel, the neuromusculoskeletal modeling and simulator (NMMS) has been widely applied in various fields. Most of the research works implements the PD-based internal model of human’s central nervous system to simulate the generated muscle activation. However, the PD-based internal models in recent works are tuned by the empirical data which requires empirical data from human subject experiments. In this dissertation, an off-policy DRL algorithm, Deep Deterministic Policy Gradient was implemented to tune the PD-based internal model of human’s central nervous system. Compared to the conventional approaches, the DRL-based auto-tuner can learn the optimal policy through trial-and-error which doesn’t require human subject experiment and empirical data. The experiment this work showed promising results of this DRL-based auto-tuner for internal-model of human’s central nervous system.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2023-1416 |
| Date | 01 January 2024 |
| Creators | Fu, Jirui |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Thesis and Dissertation 2023-2024 |
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