Abstract
Appropriate motion assistance based on understanding the patient's exact intention plays an important role in improving the patient's rehabilitation ability. This paper is a study on the development of user intention technology for a rehabilitation training system that performs dynamic rehabilitation training related to lower extremities such as posture balance and walking. The rehabilitation training system consists of a rail platform installed on the ceiling and a trolley with autonomous driving, weight support, and detection of user intention. When performing dynamic rehabilitation training such as posture balance and walking, the motion intention detection function of the patient knows the direction in which the patient is going to move in advance, and the trolley can be moved to prevent a risk such as a fall of the patient. To this end, the rehabilitation training system can measure the motion in all directions of 360∘, and visually inform the therapist or guardian by blinking the LED under the trolley whenever motion intent in each direction is detected. The user intention function of this system was tested for weight-bearing stability when performing the user's walking motion and sitting-standing motion. As a result of the experiment, it was confirmed that the RMS error was within 4% and 2%, and the peak-to-peak error was within 8% and 5%. In the future, it is expected that these results will contribute to increase the rehabilitation effect of the walking disabled. However, the results of this study were limited in their application to actual patients because they were values measured under limited experimental conditions targeting healthy subjects, so in future studies, usability tests for various users such as healthy adults, elderly people, and stroke patients will be conducted.
| Original language | English |
|---|---|
| Article number | 2240003 |
| Journal | Journal of Mechanics in Medicine and Biology |
| Volume | 22 |
| Issue number | 3 |
| DOIs | |
| State | Published - 2022.04.1 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- autonomous driving
- bodyweight support
- rehabilitation training
- User intention
Quacquarelli Symonds(QS) Subject Topics
- Biological Sciences
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