--- _id: '21264' abstract: - lang: eng text: |- Abstract Background 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. Methods 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. Results 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). Conclusion 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. author: - first_name: Alexander full_name: Boschmann, Alexander last_name: Boschmann - first_name: Dorothee full_name: Neuhaus, Dorothee last_name: Neuhaus - first_name: Sarah full_name: Vogt, Sarah last_name: Vogt - first_name: Christian full_name: Kaltschmidt, Christian last_name: Kaltschmidt - first_name: Marco full_name: Platzner, Marco last_name: Platzner - first_name: Strahinja full_name: Dosen, Strahinja last_name: Dosen citation: ama: Boschmann A, Neuhaus D, Vogt S, Kaltschmidt C, Platzner M, Dosen S. Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis. Journal of NeuroEngineering and Rehabilitation. 2021. doi:10.1186/s12984-021-00822-6 apa: Boschmann, A., Neuhaus, D., Vogt, S., Kaltschmidt, C., Platzner, M., & Dosen, S. (2021). Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis. Journal of NeuroEngineering and Rehabilitation. https://doi.org/10.1186/s12984-021-00822-6 bibtex: '@article{Boschmann_Neuhaus_Vogt_Kaltschmidt_Platzner_Dosen_2021, title={Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis}, DOI={10.1186/s12984-021-00822-6}, journal={Journal of NeuroEngineering and Rehabilitation}, author={Boschmann, Alexander and Neuhaus, Dorothee and Vogt, Sarah and Kaltschmidt, Christian and Platzner, Marco and Dosen, Strahinja}, year={2021} }' chicago: Boschmann, Alexander, Dorothee Neuhaus, Sarah Vogt, Christian Kaltschmidt, Marco Platzner, and Strahinja Dosen. “Immersive Augmented Reality System for the Training of Pattern Classification Control with a Myoelectric Prosthesis.” Journal of NeuroEngineering and Rehabilitation, 2021. https://doi.org/10.1186/s12984-021-00822-6. ieee: A. Boschmann, D. Neuhaus, S. Vogt, C. Kaltschmidt, M. Platzner, and S. Dosen, “Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis,” Journal of NeuroEngineering and Rehabilitation, 2021. mla: Boschmann, Alexander, et al. “Immersive Augmented Reality System for the Training of Pattern Classification Control with a Myoelectric Prosthesis.” Journal of NeuroEngineering and Rehabilitation, 2021, doi:10.1186/s12984-021-00822-6. short: A. Boschmann, D. Neuhaus, S. Vogt, C. Kaltschmidt, M. Platzner, S. Dosen, Journal of NeuroEngineering and Rehabilitation (2021). date_created: 2021-02-22T10:43:02Z date_updated: 2022-01-06T06:54:52Z doi: 10.1186/s12984-021-00822-6 language: - iso: eng publication: Journal of NeuroEngineering and Rehabilitation publication_identifier: issn: - 1743-0003 publication_status: published status: public title: Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis type: journal_article user_id: '35756' year: '2021' ... --- _id: '30906' abstract: - lang: eng text: "Abstract\r\n Background\r\n \ 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.\r\n \r\n Methods\r\n \ 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.\r\n \ \r\n Results\r\n \ 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).\r\n \r\n \ Conclusion\r\n 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.\r\n " article_number: '25' author: - first_name: Alexander full_name: Boschmann, Alexander last_name: Boschmann - first_name: Dorothee full_name: Neuhaus, Dorothee last_name: Neuhaus - first_name: Sarah full_name: Vogt, Sarah last_name: Vogt - first_name: Christian full_name: Kaltschmidt, Christian last_name: Kaltschmidt - first_name: Marco full_name: Platzner, Marco id: '398' last_name: Platzner - first_name: Strahinja full_name: Dosen, Strahinja last_name: Dosen citation: ama: Boschmann A, Neuhaus D, Vogt S, Kaltschmidt C, Platzner M, Dosen S. Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis. Journal of NeuroEngineering and Rehabilitation. 2021;18(1). doi:10.1186/s12984-021-00822-6 apa: Boschmann, A., Neuhaus, D., Vogt, S., Kaltschmidt, C., Platzner, M., & Dosen, S. (2021). Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis. Journal of NeuroEngineering and Rehabilitation, 18(1), Article 25. https://doi.org/10.1186/s12984-021-00822-6 bibtex: '@article{Boschmann_Neuhaus_Vogt_Kaltschmidt_Platzner_Dosen_2021, title={Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis}, volume={18}, DOI={10.1186/s12984-021-00822-6}, number={125}, journal={Journal of NeuroEngineering and Rehabilitation}, publisher={Springer Science and Business Media LLC}, author={Boschmann, Alexander and Neuhaus, Dorothee and Vogt, Sarah and Kaltschmidt, Christian and Platzner, Marco and Dosen, Strahinja}, year={2021} }' chicago: Boschmann, Alexander, Dorothee Neuhaus, Sarah Vogt, Christian Kaltschmidt, Marco Platzner, and Strahinja Dosen. “Immersive Augmented Reality System for the Training of Pattern Classification Control with a Myoelectric Prosthesis.” Journal of NeuroEngineering and Rehabilitation 18, no. 1 (2021). https://doi.org/10.1186/s12984-021-00822-6. ieee: 'A. Boschmann, D. Neuhaus, S. Vogt, C. Kaltschmidt, M. Platzner, and S. Dosen, “Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis,” Journal of NeuroEngineering and Rehabilitation, vol. 18, no. 1, Art. no. 25, 2021, doi: 10.1186/s12984-021-00822-6.' mla: Boschmann, Alexander, et al. “Immersive Augmented Reality System for the Training of Pattern Classification Control with a Myoelectric Prosthesis.” Journal of NeuroEngineering and Rehabilitation, vol. 18, no. 1, 25, Springer Science and Business Media LLC, 2021, doi:10.1186/s12984-021-00822-6. short: A. Boschmann, D. Neuhaus, S. Vogt, C. Kaltschmidt, M. Platzner, S. Dosen, Journal of NeuroEngineering and Rehabilitation 18 (2021). date_created: 2022-04-18T10:02:20Z date_updated: 2022-04-18T10:04:16Z department: - _id: '78' doi: 10.1186/s12984-021-00822-6 intvolume: ' 18' issue: '1' keyword: - Health Informatics - Rehabilitation language: - iso: eng publication: Journal of NeuroEngineering and Rehabilitation publication_identifier: issn: - 1743-0003 publication_status: published publisher: Springer Science and Business Media LLC status: public title: Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis type: journal_article user_id: '398' volume: 18 year: '2021' ...