Computer Engineering Group, Department of Computer Science, Faculty of Computer Science, Electrical Engineering and Mathematics, Paderborn University, Paderborn, Germany.
Exercise Science & Neuroscience Unit, Department Exercise and Health, Faculty of Science, Paderborn University, Paderborn, Germany.
J Neuroeng Rehabil. 2021 Feb 4;18(1):25. doi: 10.1186/s12984-021-00822-6.
Hand amputation can have a truly debilitating impact on the life of the affected person. A multifunctional myoelectric prosthesis controlled using pattern classification can be used to restore some of the lost motor abilities. However, learning to control an advanced prosthesis can be a challenging task, but virtual and augmented reality (AR) provide means to create an engaging and motivating training.
In this study, we present a novel training framework that integrates virtual elements within a real scene (AR) while allowing the view from the first-person perspective. The framework was evaluated in 13 able-bodied subjects and a limb-deficient person divided into intervention (IG) and control (CG) groups. The IG received training by performing simulated clothespin task and both groups conducted a pre- and posttest with a real prosthesis. When training with the AR, the subjects received visual feedback on the generated grasping force. The main outcome measure was the number of pins that were successfully transferred within 20 min (task duration), while the number of dropped and broken pins were also registered. The participants were asked to score the difficulty of the real task (posttest), fun-factor and motivation, as well as the utility of the feedback.
The performance (median/interquartile range) consistently increased during the training sessions (4/3 to 22/4). While the results were similar for the two groups in the pretest, the performance improved in the posttest only in IG. In addition, the subjects in IG transferred significantly more pins (28/10.5 versus 14.5/11), and dropped (1/2.5 versus 3.5/2) and broke (5/3.8 versus 14.5/9) significantly fewer pins in the posttest compared to CG. The participants in IG assigned (mean ± std) significantly lower scores to the difficulty compared to CG (5.2 ± 1.9 versus 7.1 ± 0.9), and they highly rated the fun factor (8.7 ± 1.3) and usefulness of feedback (8.5 ± 1.7).
The results demonstrated that the proposed AR system allows for the transfer of skills from the simulated to the real task while providing a positive user experience. The present study demonstrates the effectiveness and flexibility of the proposed AR framework. Importantly, the developed system is open source and available for download and further development.
手部截肢会对患者的生活产生真正的致残影响。使用模式分类控制的多功能肌电假肢可以恢复一些丧失的运动能力。然而，学习控制先进的假肢可能是一项具有挑战性的任务，但虚拟现实 (VR) 和增强现实 (AR) 提供了创建吸引人且激励人的培训的手段。
在这项研究中，我们提出了一种新颖的培训框架，该框架将虚拟元素集成到真实场景 (AR) 中，同时允许从第一人称视角查看。该框架在 13 名健全受试者和一名肢体残缺者中进行了评估，分为干预组 (IG) 和对照组 (CG)。IG 通过执行模拟别针任务接受培训，两组均使用真实假肢进行预测试和后测试。当使用 AR 进行培训时，受试者会收到有关生成的抓握力的视觉反馈。主要观察指标是在 20 分钟内成功转移的别针数量（任务持续时间），同时还记录掉落和折断的别针数量。参与者被要求对真实任务的难度（后测）、趣味性和激励性以及反馈的有用性进行评分。
在培训过程中，性能（中位数/四分位距）持续提高（4/3 至 22/4）。虽然两组在前测中的结果相似，但仅在 IG 后测中性能有所提高。此外，IG 组在后测中转移的别针明显更多（28/10.5 与 14.5/11），掉落的别针明显更少（1/2.5 与 3.5/2），折断的别针明显更少（5/3.8 与 14.5/9）与 CG 相比。IG 组的参与者分配的分数明显低于 CG（5.2±1.9 与 7.1±0.9），他们对趣味性的评价非常高（8.7±1.3）和对反馈的有用性（8.5±1.7）。
结果表明，所提出的 AR 系统允许将技能从模拟任务转移到真实任务，同时提供积极的用户体验。本研究证明了所提出的 AR 框架的有效性和灵活性。重要的是，所开发的系统是开源的，可用于下载和进一步开发。
