Feasibility of Manual Teach-and-Replay and Continuous Impedance Shaping for Robotic Locomotor Training Following Spinal Cord Injury
- Тип контента: Научная статья
- Номер документа: 6889
- Название документа: Feasibility of Manual Teach-and-Replay and Continuous Impedance Shaping for Robotic Locomotor Training Following Spinal Cord Injury
- Номер (DOI, IBSN, Патент): 10.1109/TBME.2007.910683
- Изобретатель/автор: Jeremy L. Emken, Susan J. Harkema, Janell A. Beres-Jones, Christie K. Ferreira, David J. Reinkensmeyer
- Правопреемник/учебное заведение: Biomedical Engineering Department, University of California, Department of Neurological Surgery, Frazier Rehab Institute, University of Louisville
- Дата публикации документа: 2008-01-10
- Страна опубликовавшая документ: США
- Язык документа: Английский
- Наименование изделия: Не заполнено
- Источник: IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 55, NO. 1,
- Вложения: Да
- Аналитик: Глаголева Елена
Robotic gait training is an emerging technique for retraining walking ability following spinal cord injury (SCI). A key challenge in this training is determining an appropriate stepping trajectory and level of assistance for each patient, since patients have a wide range of sizes and impairment levels. Here, we demonstrate how a lightweight yet powerful robot can record subject- specific, trainer-induced leg trajectories during manually assisted stepping, then immediately replay those trajectories. Replay of the subject-specific trajectories reduced the effort required by the trainer during manual assistance, yet still generated similar patterns of muscle activation for six subjects with a chronic SCI. We also demonstrate how the impedance of the robot can be adjusted on a step-by-step basis with an error-based, learning law. This impedance-shaping algorithm adapted the robot’s impedance so that the robot assisted only in the regions of the step trajectory where the subject consistently exhibited errors. The result was that the subjects stepped with greater variability, while still maintaining a physiologic gait pattern. These results are further steps toward tailoring robotic gait training to the needs of individual patients.
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ТОР 10 аналитиков
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Глаголева Елена - 591
Дмитрий Соловьев - 459
Helix - 218
Ридна Украина))) - 85
Наталья Черкасова - 81
max-orduan - 29
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