Stair climbing is a common activity encountered in daily living. Stair ascent is a demanding task that requires a large range of motion of the joints, strong muscle strength, good cardiovascular fitness, and fine balance control. Given this, the activity can be difficult for different populations that lack muscle strength and coordination. To train and assist people in this activity, several robotic platforms have been proposed, but these limit the natural motion of the individual. For example, these devices fix the placement of the feet and reduce the natural swing of the lower limbs. This makes it difficult to manipulate the center of mass, which is crucial to stair ascent. In this dissertation, we present a novel parallel cable-driven platform in which the end effector is the user’s pelvis; the stairmill tethered pelvic assist device. This architecture allows the user to retain their natural movement and relation between the feet and the center of mass, all while applying three dimensional forces on the pelvis during continuous stair ascent on a revolving stairmill. In this work, we show the design, fabrication, and validation of this robotic system.
Various force strategies were explored during stairmill ascent using this robotic platform. A characterization experiment was conducted to investigate gait performance and muscle coordination. Two simple interventions were tested to show the potential for long-term training program.
This work sheds light on the different strategies of stair climbing and how we can use cable driven platforms to train and assist individuals during this challenging task. The knowledge gained by this work allows for the expansion of designing training paradigms for stair climbing with natural motion. These can assist individuals in improving their quality of life.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-20fy-an81 |
| Date | January 2021 |
| Creators | Chang, Biing-Chwen |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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