Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis

Journal of NeuroEngineering and Rehabilitation

Marco Platzner¹  Alexander Boschmann¹  Strahinja Dosen²  Dorothee Neuhaus³  Sarah Vogt³  Christian Kaltschmidt³ 

[1] Computer Engineering Group, Department of Computer Science, Faculty of Computer Science, Electrical Engineering and Mathematics, Paderborn University, Paderborn, Germany;Department of Health Science and Technology, Faculty of Medicine, Aalborg University, Aalborg, Denmark;Exercise Science & Neuroscience Unit, Department Exercise and Health, Faculty of Science, Paderborn University, Paderborn, Germany;

关键词: Prosthesis control;    Pattern classification;    Myoelectric control;    Force feedback;    Augmented reality;    Training;   

DOI  :  10.1186/s12984-021-00822-6

来源: Springer

【 摘 要 】

BackgroundHand 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.MethodsIn 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.ResultsThe 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).ConclusionThe 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.

【 授权许可】

CC BY