Name
Abstract | ||
|---|---|---|
Skill training in the medical device manufacturing industry is essential to optimize and expedite the efficiency level of new workers. This process, however, gives rise to many underlying issues such as contamination and safety risks, long training period, high skill and experience requirements of operators, and greater training costs. In this paper, we proposed and evaluated a novel virtual reality (VR) training system for the assembly of hybrid medical devices. The proposed system, which is an integration of Artificial Intelligence (AI), VR and gaming concepts, is self-adaptive and autonomous. This enables the training to take place in a virtual workcell environment without the supervision of a physical trainer. In this system, a sequential framework is proposed and utilized to enhance the training through its various “game” levels of familiarity-building processes. A type of hybrid medical device: carbon nanotubes-polydimethylsiloxane (CNT-PDMS) based artificial trachea prosthesis is used as a case study in this paper to demonstrate the effectiveness of the proposed system. Evaluation results with quantitative and qualitative comparisons demonstrated that our proposed training method has significant advantages over common VR training and conventional training methods. The proposed system has addressed the underlying training issues for hybrid medical device assembly by providing trainees with effective, efficient, risk-free and low cost training. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11042-018-6216-x | Multimedia Tools Appl. |
Keywords | Field | DocType |
Interactive training environment, Virtual reality, Virtual assembly workcell, Hybrid medical device, Assembly training | Computer vision,Manufacturing,Virtual reality,Computer science,Training system,Trachea prosthesis,Human–computer interaction,Artificial intelligence,Physical trainer,Workcell | Journal |
Volume | Issue | ISSN |
77 | 23 | 1380-7501 |
Citations | PageRank | References |
4 | 0.48 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicholas Ho | 1 | 5 | 1.63 |
| Pooi-Mun Wong | 2 | 4 | 0.81 |
| Matthew Chua | 3 | 14 | 4.53 |
| Chee-Kong Chui | 4 | 245 | 38.34 |