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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2412.10154 |
| Etiquetas: |
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- Configuring a prosthetic leg is an integral part of the fitting process, but the personalization of a multi-modal powered knee-ankle prosthesis is often too complex to realize in a clinical environment. This paper develops both the technical means to individualize a hybrid kinematic-impedance controller for variable-incline walking and sit-stand transitions, and an intuitive Clinical Tuning Interface (CTI) that allows prosthetists to directly modify the controller behavior. Utilizing an established method for predicting kinematic gait individuality alongside a new parallel approach for kinetic individuality, we personalize continuous-phase/task models of joint impedance (during stance) and kinematics (during swing) using tuned characteristics exclusively from level-ground walking. To take advantage of this method, we developed a CTI that translates common clinical tuning parameters into model adjustments for the walking and sit-stand controllers. We then conducted a case study where a prosthetist iteratively tuned the powered prosthesis to an above-knee amputee participant in a simulated clinical session involving sit-stand transitions and level walking, from which incline/decline walking features were automatically calibrated. The prosthetist fully tuned the multi-activity prosthesis controller in under 20 min. Each iteration of tuning (i.e., observation, parameter adjustment, and model reprocessing) took 2 min on average for walking and 1 min on average for sit-stand. The tuned behavior changes were appropriately manifested in the commanded prosthesis torques, both at the manually tuned tasks and automatically tuned tasks (inclines). This paper introduces a clinical tuning interface that simplifies the tuning process for multimodal robotic prosthetic legs, reducing the time required from several hours to just 20 min thus improving clinical feasibility.