Generation of a Movement Scheme for Positive Training
| Autor: | Bing-Chen An, Lin Liu, Le Xie, Yunyong Shi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
030506 rehabilitation
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION upper limb Motion (physics) Field (computer science) Task (project management) rehabilitation 03 medical and health sciences 0302 clinical medicine Computer vision Exoskeleton Device Simulation Original Research 030203 arthritis & rheumatology Ground truth training business.industry General Neuroscience robot trajectory Trajectory Robot Artificial intelligence 0305 other medical science business Range of motion Neuroscience |
| Zdroj: | Frontiers in Neuroscience |
| ISSN: | 1662-453X |
| DOI: | 10.3389/fnins.2017.00096 |
| Popis: | Rehabilitation robots have been demonstrated to be an efficient tool in the field of rehabilitation training. Meanwhile, there are varieties of tasks designed for motion training. These tasks need to be transmitted to motion data for rehabilitation robots. In this paper, we designed a drinking task and captured the motion data as the ground truth, through sensors of an exoskeleton device named Neo-Arm. To verify the effectiveness of Neo-Arm, we used a Vicon system to capture the same motion task without Neo-Arm for comparison. Eight subjects participated in the experiment. The motion data of the drinking task, including the range of motion (ROM) and the velocity of each joint, are obtained. The result shows that the Neo-Arm can achieve the suitable precision and be fit for other kinds of upper limb motion tasks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|