Moving the head-neck freely is an everyday task that a healthy person takes for granted. Such a simple movement, however, may be very challenging for individuals with neurological disorders such as amyotrophic lateral sclerosis. These individuals often do not have enough neck muscle strength to stabilize the head at the upright neutral or to move it in a controlled manner. Static braces are commonly prescribed to these patients. However, these braces often fix the head at a single configuration, which makes them uncomfortable to wear for an extended period of time.
In this thesis, a robotic neck brace is developed. It accommodates three rotations and covers roughly 70% range of motion of the head-neck of a typical able-bodied adult. The hardware is lightweight (1.5 kilogram) and wearable, with a pair of pads and a soft band attached to the shoulders and the forehead, respectively. A parallel mechanism connecting the shoulder pads and the headband was designed to meet the empirical human movement data. This design choice is novel where the parasitic motion (translation of the head) was parameterized and optimized to address misalignment between the robot and the user's head.
A user can control this neck brace to assist intended head-neck movement through input devices, including hand-held joysticks, keyboards, and eye-trackers. This provides a potential solution to remediate head drop. Additionally, this robotic brace is developed into a versatile platform to train and study head-neck movements. The robot was designed to be highly transparent to the user and features different force controllers. Therefore, it can be used to assess the free movement of the head-neck and mimic different interactions between a therapist and a patient. The modalities of this neck brace have been validated with different users. To the best of our knowledge, this robotic neck brace is the first in the literature to assist, train, and study head-neck movements.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-vbwn-0c76 |
| Date | January 2019 |
| Creators | Zhang, Haohan |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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