Refine
Year of publication
Institute
- Fachbereich Medizintechnik und Technomathematik (1934)
- Fachbereich Elektrotechnik und Informationstechnik (1150)
- Fachbereich Wirtschaftswissenschaften (1121)
- Fachbereich Energietechnik (1067)
- Fachbereich Chemie und Biotechnologie (897)
- Fachbereich Maschinenbau und Mechatronik (812)
- Fachbereich Luft- und Raumfahrttechnik (769)
- Fachbereich Bauingenieurwesen (664)
- IfB - Institut für Bioengineering (627)
- INB - Institut für Nano- und Biotechnologien (586)
Has Fulltext
- no (9330) (remove)
Language
Document Type
- Article (5531)
- Conference Proceeding (1421)
- Book (1062)
- Part of a Book (567)
- Patent (177)
- Bachelor Thesis (169)
- Report (83)
- Doctoral Thesis (82)
- Conference: Meeting Abstract (76)
- Other (67)
Keywords
- Illustration (10)
- Nachhaltigkeit (10)
- Corporate Design (9)
- Erscheinungsbild (8)
- Gamification (8)
- Redesign (7)
- Animation (6)
- Datenschutz (6)
- Deutschland (6)
- Digitalisierung (6)
Adapting augmented reality systems to the users’ needs using gamification and error solving methods
(2021)
Animations of virtual items in AR support systems are typically predefined and lack interactions with dynamic physical environments. AR applications rarely consider users’ preferences and do not provide customized spontaneous support under unknown situations. This research focuses on developing adaptive, error-tolerant AR systems based on directed acyclic graphs and error resolving strategies. Using this approach, users will have more freedom of choice during AR supported work, which leads to more efficient workflows. Error correction methods based on CAD models and predefined process data create individual support possibilities. The framework is implemented in the Industry 4.0 model factory at FH Aachen.
To prevent the reduction of muscle mass and loss of strength coming along with the human aging process, regular training with e.g. a leg press is suitable. However, the risk of training-induced injuries requires the continuous monitoring and controlling of the forces applied to the musculoskeletal system as well as the velocity along the motion trajectory and the range of motion. In this paper, an adaptive norm-optimal iterative learning control algorithm to minimize the knee joint loadings during the leg extension training with an industrial robot is proposed. The response of the algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee and compared to the results of a higher-order iterative learning control algorithm, a robust iterative learning control and a recently proposed conventional norm-optimal iterative learning control algorithm. Although significant improvements in performance are made compared to the conventional norm-optimal iterative learning control algorithm with a small learning factor, for the developed approach as well as the robust iterative learning control algorithm small steady state errors occur.