Name
Title | ||
|---|---|---|
Electrotactile feedback in a virtual hand rehabilitation platform: evaluation and implementation |
Abstract | ||
|---|---|---|
Tactile feedback plays an important role in hand manipulation, especially in the grasping process which is one of the major functions of the hand. However, few commercially available prosthetic hands or hand motor function rehabilitation systems are equipped with tactile feedback. The absence of suitable tactile feedback modules leads to an inferior rehabilitation performance with a large burden on user training and compromised usability. Thus, it is challenging and essential to integrate a proper tactile feedback module with the existing hand rehabilitation systems to achieve a better control performance and accelerate the rehabilitation process. This paper focuses on the implementation and evaluation of the electrotactile feedback (EF) enhanced rehabilitation system. A virtual hand rehabilitation platform is proposed comprising an surface electromyography (sEMG) acquisition module, an electrotactile stimulation module, a virtual environment with sEMG-driven humanlike hand and numerical feedbacks of grasping force and fingertip deformation, where a closed-loop control is formed. Three different feedback conditions including visual feedback (VF), EF, and no feedback (NF) are compared based on the proposed platform. Experiments were conducted on 10 able-bodied subjects, and multiple quantitative metrics for the rehabilitation performance evaluation including training burden estimation and success rate (SR) of tasks were adopted. Results indicate that the integration of EF is helpful to both reduce the rehabilitation duration and improve the virtual grasping SR in comparison with the NF condition while possessing a better practicality over VF. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TASE.2018.2882465 | IEEE Transactions on Automation Science and Engineering |
Keywords | Field | DocType |
Tactile sensors,Grasping,Prosthetics,Virtual environments,Force,Visualization,Muscles | Hand manipulation,Rehabilitation,Virtual machine,Computer science,Usability,Control engineering,Hand grasp,Human–computer interaction,Motor function,Encoding (memory),Tactile sensor | Journal |
Volume | Issue | ISSN |
16 | 4 | 1545-5955 |
Citations | PageRank | References |
3 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kairu Li | 1 | 3 | 0.70 |
| Peter Ralph Boyd | 2 | 3 | 0.36 |
| Yu Zhou | 3 | 98 | 22.73 |
| Zhaojie Ju | 4 | 284 | 48.23 |
| Honghai Liu | 5 | 1974 | 178.69 |